Systems and methods to increase capacity in optical networks based on excess margin

ABSTRACT

A computer-implemented method to increase capacity of an optical network based on overall excess margin in the optical network includes determining an objective function based on data associated with a plurality of optical signals in the optical network, each of the optical signals between modems in the optical network, wherein an input to the objective function comprises how much margin the optical signals have until Forward Error Correction (FEC) limits are reached; performing an optimization of the objective function based on changing a plurality of parameters of the optical signals; and causing changes to settings of a subset of the modems based on the performing to change the capacity of the optical network.

CROSS-SECTION TO RELATED APPLICATION(S)

The present non-provisional patent/patent application is a continuationof U.S. Provisional patent Ser. No. 14/536,871 filed Nov. 10, 2014 andentitled “MARGIN-BASED OPTIMIZATION SYSTEMS AND METHODS IN OPTICALNETWORKS BY INTENTIONALLY REDUCING MARGIN” which claims priority to U.S.Provisional Patent Ser. No. 62/000,168 filed May 19, 2014 and entitled“MARGIN-BASED EQUALIZATION SYSTEMS AND METHODS IN OPTICAL NETWORKS,” thecontents of each are incorporated by reference herein.

The present non-provisional patent/patent applications relates to thefollowing commonly-assigned U.S. patent applications, the contents ofwhich are incorporated by reference herein:

Docket No. Filing Date Serial No. Title 10.2037 Aug. 26, 2011 13/218,759CONCATENATED OPTICAL SPECTRUM TRANSMISSION SYSTEMS AND METHODS 10.2088Feb. 13, 2012 13/372,013 HIGH SPEED OPTICAL COMMUNICATION SYSTEMS ANDMETHODS WITH FLEXIBILE BANDWIDTH ADAPTATION 10.2187 Feb. 10, 201414/176,908 SYSTEMS AND METHODS FOR MANAGING EXCESS OPTICAL CAPACITY ANDMARGIN IN OPTICAL NETWORKS 10.2187.CIP Nov. 6, 2014 14/534,657 HITLESSMODULATION SCHEME CHANGE SYSTEMS AND METHODS IN OPTICAL NETWORKS

FIELD OF THE DISCLOSURE

The present disclosure relates generally to optical network systems andmethods. More particularly, the present disclosure relates tomargin-based optimization systems and methods in optical networks.

BACKGROUND OF THE DISCLOSURE

Optical network modeling and engineering are concerned with placingviable services (on wavelengths) into a network. Conventionally, linkmodeling and engineering is performed for forecast tolerant engineering,i.e., all wavelengths, in a Wavelength Division Multiplexed (WDM) aretreated equally and any wavelength placed in the network is guaranteedto work (based on the engineering), regardless of conditions, i.e. aworst-case engineering approach. The corollary to this is that initialwavelengths in new deployments will have a large amount of excess marginor wavelengths in a fully-utilized (not all wavelengths present on alllinks due to contention or blocking) system have excess margin. Asoptical networks progress, conventional transmitters/receivers (TX/RX),which typically utilized simple on-off keying, are evolving to advancedoptical Modulators/Demodulators (modems) with adaptable modulationformats. Other modems (e.g., cell phones, digital subscriber loopmodems, cable modems, etc.) perform optimization to provide additionalcapacity based on current conditions. However, conventionally, opticalnetworks have not performed optimization except for the initialviability determination during link modeling and engineering. Note,while the other modems listed above can perform their optimization withtradeoffs independently on multiple wavelengths (owing to a linearmedium), optical networks must perform these optimizations for a fullset of wavelengths due to nonlinear interactions in optical fiber and toensure proper operation at worst case, i.e. full-fill. Stateddifferently, optical network optimization is vastly different fromoptimizing in the other modems described above. Additionally, opticalnetworks can differentiate between wavelengths that may or may not needadditional capacity (based on the underlying optical modem and servicebeing transported) while the other modems seek to maximize capacity ontheir linear medium.

It is expected that optical network deployment will move away fromup-front engineering for worst-case, end-of-life conditions towardsautomatic optimization for current conditions, a process that cancontinually run over the life of the deployment. This will provideadditional opportunities for more bandwidth, without increasing capitalcosts, as optical equipment is run based on a current optimizationrather than a forecast tolerant, end-of-life optimization. In thismanner, it is important to determine systems and methods forhour-by-hour optimization of optical networks across 15+ years of change(or whatever time period the equipment is engineered and deployed to).This problem statement can be summarized as how to understand mechanismsto optimize all parameters available in highly nonlinear opticalnetworks.

Accordingly, there is a need for margin-based optimization systems andmethods based on the characteristics of optical networks andunderstanding how these can be used to maximize bandwidth based oncurrent conditions.

BRIEF SUMMARY OF THE DISCLOSURE

In an exemplary embodiment, a computer-implemented method is implementedin one of a Network Management System (NMS), an Element ManagementSystem (EMS), a Software Defined Networking (SDN) controller, and aserver executing an SDN application, to increase capacity of one or morelinks in an optical network. The method includes determining Net SystemMargin including a metric of overall excess margin in the opticalnetwork until a Forward Error Correction (FEC) limit is reached;performing an optimization of a plurality of parameters of the opticalnetwork to determine which settings are appropriate in the opticalnetwork to provide the increased capacity and to consume at least partof the Net System Margin; and causing a plurality of modems in theoptical network to change settings based on the optimization to providethe increased capacity. The determined Net System Margin can be based onone or more of measured data and estimated data from nodes in theoptical network communicated to one of the NMS, the EMS, the SDNcontroller, and the server, wherein the measured data and the estimateddata is utilized to determine real-time margin based on non-linearimpairments, link loss, dispersion, and error rates.

The settings can include one or more of per channel power, amplifiergain, wavelength, modulation format, precompensation, spectral width,spectral shape, spectral spacing, superchannels, baud rate, and FECparameters. The computer-implemented method can further includecomputing and displaying a dashboard showing the Net System Margin andone or more additional metrics of the optical network, wherein the oneor more additional metrics include health detailing a view ofnon-blocked restoration paths and network resiliency and restorability,throughput including how much data is currently being transported in theoptical network, and excess bandwidth including how much excess capacityis available in the optical network. The optimization can include one ormore of a single channel optimization, a gridded full optimization, asuperchannel full optimization, a gridded mesh optimization, a gridlessmesh optimization, and a genetic algorithm optimization. Theoptimization can include a multi-channel, non-linear aware, linkmodeling routine which uses an objective function. The optimization canhave a plurality of assumptions included based on the optical network toconstrain inputs. The settings can be adjusted for wavelengths to changeoptical power, bit rate, baud rate, and modulation format and foroptical channels to change parameters associated with paths thewavelengths traverses such as frequency spacing, spectrum amount, andoptical component settings.

In another exemplary embodiment, a system includes one of a NetworkManagement System (NMS), an Element Management System (EMS), a SoftwareDefined Networking (SDN) controller, and a server executing an SDNapplication, adapted to increase capacity of one or more links in anoptical network based on overall excess margin in the optical network.The system includes a network interface and a processor communicativelycoupled to one another; and memory storing instructions that, whenexecuted, cause the processor to determine Net System Margin including ametric of overall excess margin in the optical network until a ForwardError Correction (FEC) limit is reached, perform an optimization of aplurality of parameters of the optical network to determine whichsettings are appropriate in the optical network to provide the increasedcapacity and to consume at least part of the Net System Margin, andcommunicate via the network interface to the optical network to cause aplurality of modems in the optical network to change settings based onthe optimization to provide the increased capacity. The determined NetSystem Margin can be based on one or more of measured data and estimateddata from nodes in the optical network communicated to one of the NMS,the EMS, the SDN controller, and the server, wherein the measured dataand the estimated data is utilized to determine real-time margin basedon non-linear impairments, link loss, dispersion, and error rates.

The settings can include one or more of per channel power, amplifiergain, wavelength, modulation format, precompensation, spectral width,spectral shape, spectral spacing, superchannels, baud rate, and FECparameters. The memory storing instructions that, when executed, canfurther cause the processor to compute and display a dashboard showingthe Net System Margin and one or more additional metrics of the opticalnetwork, wherein the one or more additional metrics include healthdetailing a view of non-blocked restoration paths and network resiliencyand restorability, throughput including how much data is currently beingtransported in the optical network, and excess bandwidth including howmuch excess capacity is available in the optical network. Theoptimization can include one or more of a single channel optimization, agridded full optimization, a superchannel full optimization, a griddedmesh optimization, a gridless mesh optimization, and a genetic algorithmoptimization. The optimization can include a multi-channel, non-linearaware, link modeling routine which uses an objective function. Theoptimization can have a plurality of assumptions included based on theoptical network to constrain inputs. The settings can be adjusted forwavelengths to change optical power, bit rate, baud rate, and modulationformat and for optical channels to change parameters associated withpaths the wavelengths traverses such as frequency spacing, spectrumamount, and optical component settings.

In a further exemplary embodiment, an optical network with a systemadapted to increase capacity of one or more links based on overallexcess margin in the optical network includes a plurality of nodesinterconnected optically to one another by a plurality of links; and aserver communicatively coupled to one or more of the nodes, wherein theserver includes one of a Network Management System (NMS), an ElementManagement System (EMS), a Software Defined Networking (SDN) controller,and a server executing an SDN application. The server is adapted todetermine Net System Margin comprising a metric of overall excess marginin the optical network until a Forward Error Correction (FEC) limit isreached, perform an optimization of a plurality of parameters of theoptical network to determine which settings are appropriate in theoptical network to provide the increased capacity and to consume atleast part of the Net System Margin, and communicate via the networkinterface to the optical network to cause a plurality of modems in theoptical network to change settings based on the optimization to providethe increased capacity.

The determined Net System Margin can be based on one or more of measureddata and estimated data from nodes in the optical network communicatedto one of the NMS, the EMS, the SDN controller, and the server, whereinthe measured data and the estimated data is utilized to determinereal-time margin based on non-linear impairments, link loss, dispersion,and error rates. The settings can include one or more of per channelpower, amplifier gain, wavelength, modulation format, precompensation,spectral width, spectral shape, spectral spacing, superchannels, baudrate, and FEC parameters. The optimization can include one or more of asingle channel optimization, a gridded full optimization, a superchannelfull optimization, a gridded mesh optimization, a gridless meshoptimization, and a genetic algorithm optimization.

In a further exemplary embodiment, a method of optimizing capacity of anoptical network includes identifying a first wavelength with anassociated target capacity; determining that the first wavelength hasinsufficient capability to operate at the associated target capacity;and adjusting one or more wavelengths to increase capability of thefirst wavelength such that the first wavelength can operate at theassociated target capacity. The adjusting can utilize any one ofmodifying average power, changing wavelength, changing modulation, andchanging precompensation. The determining the insufficient capacity canbe comparing one or more link parameters associated with the firstwavelength to thresholds and deriving a Net System Margin. One or morelink parameters are any of additive noise, Cross-Phase Modulation,Cross-Polarization Modulation, and spectral width. The one or more linkparameters can be measured by a modem associated with the firstwavelength. The insufficient capability can be based on any of noisemargin and spectral width. The insufficient capability can be not enoughto either presently meet a performance for the associated targetcapacity or to meet a performance for the associated target capacity ata future time. The adjusting can utilize changing modulation to achieveany one of reduced nonlinear aggression, reduced spectral width, andchanged spectral shape. The method can include performing a nonlinearoptimization to determine adjustments to the one or more wavelengths bymodeling modem bit rate, Optical Signal to Noise Ratio (OSNR), andwhether or not a signal can support additional capacity as realfunctions in the nonlinear optimization. The adjusting can be simulatedin an application prior to operation on nodes in the optical network.

In another exemplary embodiment, a controller for optimizing capacity ofan optical network a processor communicatively coupled to a networkinterface; and memory storing instructions that, when executed, causethe processor to identify a first wavelength with an associated targetcapacity, determine that the first wavelength has insufficientcapability to operate at the associated target capacity, and cause orsimulate adjustment of one or more wavelengths to increase capability ofthe first wavelength such that the first wavelength can operate at theassociated target capacity. The adjustment can utilize any of modifyingaverage power, changing wavelength, changing modulation, and changingprecompensation. The insufficient capacity can be determined bycomparing one or more link parameters associated with the firstwavelength to thresholds and deriving a Net System Margin. The one ormore link parameters can be any of additive noise, Cross-PhaseModulation, Cross-Polarization Modulation, and spectral width. The oneor more link parameters can be measured by a modem associated with thefirst wavelength. The insufficient capability can be based on any ofnoise margin and spectral width. The insufficient capability can be notenough to either presently meet a performance for the associated targetcapacity or to meet a performance for the associated target capacity ata future time. The adjustment can utilize changing modulation to any ofreduce nonlinear aggression, reduce spectral width, and change spectralshape. The memory storing instructions that, when executed, furthercause the processor to: perform a nonlinear optimization to determineadjustments to the one or more wavelengths by modeling modem bit rate,Optical Signal to Noise Ratio (OSNR), and whether or not a signal cansupport additional capacity as real functions in the nonlinearoptimization.

In a further exemplary embodiment, an optical network a plurality ofnodes interconnected by a plurality of links; and a controllercommunicatively couple to one or more of the plurality of nodes, whereinthe controller is configured to identify a first wavelength, between twoof the plurality of nodes, with an associated target capacity, determinethat the first wavelength has insufficient capability to operate at theassociated target capacity, and cause or simulate adjustment of one ormore wavelengths, on some or all links associated with the firstwavelength, to increase capability of the first wavelength such that thefirst wavelength can operate at the associated target capacity.

In another exemplary embodiment, a method of optimizing capacity of anoptical network, through intentionally reducing margin on one or morewavelengths includes identifying a first wavelength capable of usingexcess capacity; determining the one or more wavelengths that have extramargin; adjusting at least one of the one or more wavelengths to reduceassociated margin to a nominal margin so as to increase supportablecapacity of the first wavelength; and increasing capacity of the firstwavelength based on the supportable capacity. The adjusting can utilizeany one of modifying average power, changing wavelength, changingmodulation, and changing precompensation. The reduction to the nominalmargin can be based on comparing one or more link parameters associatedwith one or more wavelengths used to derive a Net System Margin. The oneor more link parameters can be any of additive noise, Cross-PhaseModulation, Cross-Polarization Modulation, and spectral width. The oneor more link parameters can be measured by a modem associated with theone or more wavelengths. The adjusting can utilize changing modulationto achieve any one of reduced nonlinear aggression, reduced spectralwidth, and changed spectral shape. The method can further includeperforming a nonlinear optimization to determine adjustments to the oneor more wavelengths by modeling modem bit rate, Optical Signal to NoiseRatio (OSNR), and whether or not a signal can support additionalcapacity as real functions in the nonlinear optimization. The adjustingcan be simulated in an application prior to operation on nodes in theoptical network.

In another exemplary embodiment, a controller for optimizing capacity ofan optical network, through intentionally reducing margin on one or morewavelengths, the controller includes a processor communicatively coupledto a network interface; and memory storing instructions that, whenexecuted, cause the processor to identify a first wavelength capable ofusing excess capacity, determine the one or more wavelengths have extramargin, adjust the one or more wavelengths to reduce associated marginto a nominal margin amount so as to increase supportable capacity of thefirst wavelength, and increase capacity of the first wavelength based onthe supportable capacity. The one or more wavelengths can be adjustedthrough any of modifying average power, changing wavelength, changingmodulation, and changing precompensation. The reduction to the nominalmargin can be based on comparing one or more link parameters associatedwith one or more wavelengths used to derive a Net System Margin. The oneor more link parameters can be any of additive noise, Cross-PhaseModulation, Cross-Polarization Modulation, and spectral width. The oneor more link parameters can be measured by a modem associated with theone or more wavelengths. The one or more wavelengths can be adjusted bychanging modulation to any of reduce nonlinear aggression, reducespectral width, and change spectral shape. The memory storinginstructions that, when executed, can further cause the processor to:perform a nonlinear optimization to determine adjustments to the one ormore wavelengths by modeling modem bit rate, Optical Signal to NoiseRatio (OSNR), and whether or not a signal can support additionalcapacity as real functions in the nonlinear optimization. The adjustingcan be simulated in an application prior to operation on nodes in theoptical network. The controller can be a Software Defined Networking(SDN) controller.

In another further exemplary embodiment, an optical network includes aplurality of nodes interconnected by a plurality of links; and acontroller communicatively couple to one or more of the plurality ofnodes, wherein the controller is configured to identify a firstwavelength capable of using excess capacity, determine the one or morewavelengths have extra margin, adjust the one or more wavelengths toreduce associated margin to a nominal margin amount so as to increasesupportable capacity of the first wavelength, and increase capacity ofthe first wavelength based on the supportable capacity. The controlleris further configured to: perform a nonlinear optimization to determineadjustments to the one or more wavelengths by modeling modem bit rate,Optical Signal to Noise Ratio (OSNR), and whether or not a signal cansupport additional capacity as real functions in the nonlinearoptimization. The controller can be a Software Defined Networking (SDN)controller.

In yet another exemplary embodiment, a method of increasing thesupportable capacity from a first point to a second point in an opticalnetwork includes identifying a first optical signal that occupies afirst portion of optical spectrum from the first point to the secondpoint; identifying a second optical signal that occupies a secondportion of the optical spectrum from the first point to the secondpoint, wherein the second portion is adjacent to the first portion;adjusting the second optical signal to minimize part of or remove all ofthe second portion that is adjacent to the first optical signal toprovide a freed up portion of the second portion; and adjusting thefirst optical signal to occupy some or all of the freed up portion. Thesecond optical signal can co-propagate with the first optical signalthrough a first node of the optical network and separately propagatesthrough a second node of the optical network. The adjusting the firstoptical signal can be one of converting the first optical signal to asuperchannel and increasing a modulation symbol rate. The adjusting thesecond optical signal can be one of moving the second optical signal toa disjoint portion of the spectrum from the first portion and reducing aspectral width of the second optical signal. The adjusting the secondoptical signal can include identifying a new path, increasing supportedcapacity on the new path, and transferring the freed up portion to thenew path. The increasing supported capacity on the new path can be anyone of reducing nonlinear effects, increasing spectral width, andincreasing power. The increasing supported capacity on the new path canbe achieving any one of reducing nonlinear effects, increasing spectralwidth, and increasing power. The new path can be partially disjoint witha route previously taken by the second optical signal. The method canfurther include performing a nonlinear optimization to determineadjustments to the first optical signal and the second optical signal bymodeling modem bit rate, Optical Signal to Noise Ratio (OSNR), andwhether or not a signal can support additional capacity as realfunctions in the nonlinear optimization. The adjusting can be simulatedin an application prior to operation on nodes in the optical network.

In yet another exemplary embodiment, a controller for optimizingcapacity of an optical network, through intentionally reducing margin onone or more wavelengths, the controller includes a processorcommunicatively coupled to a network interface; and memory storinginstructions that, when executed, cause the processor to identify afirst optical signal that occupies a first portion of optical spectrumfrom the first point to the second point, identify a second opticalsignal that occupies a second portion of the optical spectrum from thefirst point to the second point, wherein the second portion is adjacentto the first portion, adjust the second optical signal to minimize partof or remove all of the second portion that is adjacent to the firstoptical signal to provide a freed up portion of the second portion, andadjust the first wavelength to occupy some or all of the freed upportion. The second optical signal can co-propagate with the firstoptical signal through a first node of the optical network andseparately propagates through a second node of the optical network. Thefirst optical signal can be adjusted by one of converting the firstoptical signal to a superchannel and increasing a modulation symbolrate. The second optical signal can be adjusted by one of moving thesecond optical signal to a disjoint portion of the spectrum from thefirst portion and reducing a spectral width of the second opticalsignal. The second optical signal can be adjusted by identifying a newpath, increasing supported capacity on the new path, and transferringthe freed up portion to the new path. The increasing supported capacityon the new path can be any of reducing nonlinear effects, increasingspectral width, and increasing power. The increasing supported capacityon the new path can be any of reducing nonlinear effects, increasingspectral width, and increasing power. The new path can be partiallydisjoint with a route previously taken by the second optical signal. Thememory storing instructions that, when executed, can further cause theprocessor to: perform a nonlinear optimization to determine adjustmentsto the second optical signal and/or the first optical signal by modelingmodem bit rate, Optical Signal to Noise Ratio (OSNR), and whether or nota signal can support additional capacity as real functions in thenonlinear optimization.

In yet another further exemplary embodiment, an optical network includesa plurality of nodes interconnected by a plurality of links; and acontroller communicatively couple to one or more of the plurality ofnodes, wherein the controller is configured to identify a first opticalsignal that occupies a first portion of optical spectrum from a firstnode to a second node over some of the plurality of links, identify asecond optical signal that occupies a second portion of the opticalspectrum from the first point to the second point, wherein the secondportion is adjacent to the first portion, adjust the second wavelengthto minimize part of or remove all of the second portion that is adjacentto the first optical signal to provide a freed up portion of the secondportion, and adjust the first optical signal to occupy some or all ofthe freed up portion.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated and described herein withreference to the various drawings, in which like reference numbers areused to denote like system components/method steps, as appropriate, andin which:

FIG. 1 is a network diagram of an exemplary network with fiveinterconnected sites in a meshed optical network;

FIG. 2 is a logical network diagram of the network of FIG. 1 with thelinks logically shown carrying various wavelengths;

FIG. 3 is a logical network diagram of a subset of the network of FIG. 2showing a subset of the sites and three exemplary wavelengths;

FIG. 4 is a graph of three exemplary configurations of an optical modemshowing relative noise tolerance (dB) versus bits per dual polarizationsymbol;

FIG. 5 is a diagram of a dashboard with metrics associated with anoptical network;

FIGS. 6-10 are spectral plots logically illustrating variousoptimization metrics;

FIG. 11 is a flowchart of a margin-based optimization method;

FIG. 12 is a network diagram of a network with an interconnectedphotonic mesh of nodes;

FIGS. 13-21 are various screens of operations with an application usingthe network of FIG. 12 for optimization thereon;

FIGS. 22-23 are spectral diagrams of optical spectrum before and afteroptimization;

FIG. 24 is a flow chart of a capacity boosting method;

FIG. 25 is a flow chart of a “Robin Hood” method which intentionallyreduces margin on wavelengths for the benefit of other wavelengths;

FIG. 26 is a flow chart of an unblocking method to increase thesupportable capacity from a first point to a second point in an opticalnetwork;

FIG. 27 is a flow chart of a method of planning wavelength assignmentsin a WDM mesh optical network;

FIG. 28 is a flow chart of a method of minimizing the cost of a WDM meshoptical network;

FIG. 29 is a block diagram of an exemplary network element for use withthe systems and methods described herein;

FIG. 30 is a block diagram illustrates a controller to provide controlplane processing and/or operations, administration, maintenance, andprovisioning (OAM&P) for the network element of FIG. 29; and

FIG. 31 is a block diagram of a server.

DETAILED DESCRIPTION OF THE DISCLOSURE

In various exemplary embodiments, margin-based optimization systems andmethods in optical networks are described. The systems and methods cannot only attempt to increase capacity, but also freezes capacity andeven reduces optical footprint with a reduction of impact function. Thatis, the systems and methods propose to optimize as well as penalize(de-optimize) wavelengths for the overall benefit in the opticalnetwork, i.e., consume as much margin as possible for additionalcapacity where it can be harvested. The systems and methods improvespecific wavelengths and penalize other wavelengths to improve overallmesh network capacity, treat modems that look the same differently basedon an ability to provide additional revenue generating services byproviding network capacity, apply a different optical networkoptimization criteria based on modem traffic carrying potential, and useoptical modem traffic carrying potential as a mechanism to determinewhether to improve or penalize a specific wavelength service. Note, asdescribed herein, a wavelength, a signal, or an optical signal can beused interchangeably to denote light having been modulated to carryinformation over a medium within a channel or optical channel. Thevarious optimizations described herein contemplate adjustments to thewavelength (signal) as well as to the channel (optical channel). Forexample, adjustments to the wavelength include changes to the parametersof the signal such as optical power, bit rate, baud rate, modulationformat, etc. Adjustments to the channel include changes to theparameters associated with the path the signal traverses such asfrequency spacing, amount of spectrum, amplifier/WSS/VOA settings, etc.That is, anything associated with the signal or the channel can bemodified with the objective to carry as much data as possible givencurrent constraints.

Key takeaways for the margin-based optimization systems and methodsinclude: 1) networks can be more efficient than they are conventionallyoperated; flexible Layer 0 (photonics and modems) can be programmed toincrease network capacity; advanced control, modeling and management cantake advantage of the increased network capacity; and hybridpacket-optical systems can exploit the increased network capacity foradditional opportunities, e.g. both guaranteed and best effortbandwidth. The margin-based optimization systems and methods canintroduce equalization/optimization into existing photonic controlsystems and methods.

In an exemplary embodiment, a method determining for a plurality ofwavelength services in a network which support additional networkcapacity; computing or retrieving Required Optical Signal to Noise Ratio(ROSNR) for each of the plurality of wavelength services; and performingan equalization on the plurality of wavelength services to maximizecapacity for those which support the additional network capacity.

Exemplary Optical Network

Referring to FIG. 1, in an exemplary embodiment, a network diagramillustrates an exemplary network 100 with five interconnected sites 110a, 110 b, 110 c, 110 d, 110 e. The sites 110 are interconnected througha plurality of links 120. Each of the sites 110 can include a switch 122and one or more WDM network elements 124. The switch 122 is configuredto provide services at Layers 1 (e.g., OTN/SONET/SDH) and/or Layer 2(e.g., Ethernet). The WDM network elements 124 provide the photoniclayer (e.g., Layer 0) and various functionality associated therewith(e.g., multiplexing, amplification, optical routing, wavelengthconversion/regeneration, local add/drop, etc.) including photoniccontrol. Of note, while shown separately, those of ordinary skill in theswitch 122 and the WDM network elements 124 may be realized in a samenetwork element. The photonic layer and the photonic control operatingthereon can also include intermediate amplifiers and/or regenerators onthe links 120 which are omitted for illustration purposes. The network100 is illustrated, for example, as an interconnected mesh network, andthose of ordinary skill in the art will recognize the network 100 caninclude other architectures, with additional sites 110 or with lessnodes sites, with additional network elements and hardware, etc. Thenetwork 100 is presented herein as an exemplary embodiment for themargin-based optimization systems and methods.

The sites 110 communicate with one another optically over the links 120.The sites 110 can be network elements which include a plurality ofingress and egress ports forming the links 120. Further, the nodes 110can include various degrees, i.e. the site 110 c is a one degree node,the sites 110 a, 110 d are two degree nodes, the site 110 e is a threedegree node, and the site 110 b is a four degree node. The number ofdegrees is indicative of the number of adjacent nodes at each particularnode. The network 100 includes a control plane 140 operating on and/orbetween the switches 122 at the sites 110 a, 110 b, 110 c, 110 d, 110 e.The control plane 140 includes software, processes, algorithms, etc.that control configurable features of the network 100, such asautomating discovery of the switches 122, capacity of the links 120,port availability on the switches 122, connectivity between ports;dissemination of topology and bandwidth information between the switches122; calculation and creation of paths for connections; network levelprotection and restoration; and the like. In an exemplary embodiment,the control plane 140 can utilize Automatically Switched Optical Network(ASON), Generalized Multiprotocol Label Switching (GMPLS), OpticalSignal and Routing Protocol (OSRP) (from Ciena Corporation), or thelike. Those of ordinary skill in the art will recognize the network 100and the control plane 140 can utilize any type control plane forcontrolling the switches 122 and establishing connections therebetween.

Service routing in the control plane 140 is well known. A path (e.g., asubnetwork connection (SNC) or label switched path (LSP)) is consideredvalid for connection setup based on the availability of the switch 122,the links 120, and sufficient bandwidth available thereon. Photonicnetworks, i.e. Layer 0 and the wavelength interconnectivity of the WDMnetwork elements 124, introduce additional complexity of successfullysetting up a service up. This can require that all Layer 0 services arepre-planned and/or managed manually. For example, potential paths forservices at the photonic layer can be pre-planned by modeling themoffline using a static snapshot of the network state to ensure that thecomputed paths are optically viable in terms of reach, nonlineareffects, dispersion, wavelength contention/blocking, etc. Here, theforecast tolerant engineering ensures that each wavelength placed intoservice will work in a worst case Optical Signal to Noise Ratio (OSNR)leading to potential excess margin.

The network 100 can include photonic control 150 which can be viewed asa control plane and/or control algorithm/loop for managing wavelengthsfrom a physical perspective at Layer 0. In one aspect, the photoniccontrol 150 is configured to add/remove wavelengths from the links in acontrolled manner to minimize impacts to existing, in-servicewavelengths. For example, the photonic control 150 can adjust modemlaunch powers, optical amplifier gain, variable optical attenuator (VOA)settings, wavelength selective switch (WSS) parameters, etc. In thesystems and method described herein, the photonic control 150 is adaptedto also perform network optimization on the links 120. This optimizationcan also include re-optimization where appropriate. In the systems andmethods, the photonic control 150 can adjust the modulation format, baudrate, frequency, wavelength, spectral width, etc. of the modems inaddition to the aforementioned components at the photonic layer.

The network 100 can also include a Software Defined Networking (SDN)controller 160. SDN allows management of network services throughabstraction of lower level functionality. This is done by decoupling thesystem that makes decisions about where traffic is sent (SDN controlthrough the SDN controller 160) from the underlying systems that forwardtraffic to the selected destination (i.e., the physical equipment in thenetwork 100). Work on SDN calls for the ability to centrally programprovisioning of forwarding on the network 100 in order for more flexibleand precise control over network resources to support new services. TheSDN controller 160 is a processing device that has a global view of thenetwork 100. Additionally, the SDN controller 160 can include or connectto SDN applications which can utilize the data from the SDN controller160 for various purposes. In an exemplary embodiment, the SDNapplications include a margin optimization application which isdescribed in detail herein.

Referring to FIG. 2, in an exemplary embodiment, a logical networkdiagram illustrates the network 100 of FIG. 1 with the links 120logically shown carrying various wavelengths. The links 120 are shownwith an optical fiber 200 which can include any type of optical fiber.For example, the optical fiber 200 can include a useable opticalspectrum of 1530 nm to 1565 nm (C-Band). Of course, other spectrums arecontemplated. The optical fiber 200 can be a flexible grid, a fixedgrid, or a combination across the optical spectrum. Thus, each of thelinks 120 and their associated optical fiber 200 can support a fixed orvariable number of wavelengths 210 (wavelengths can also be referred toas optical signals). The wavelengths 210 traverse a channel whichcarries an underlying service between two of the sites 110 in thenetwork. Each of the wavelengths 210 is formed by optical modems at twosites 110 where the channel is added/dropped (or regenerated). Since thenetwork 100 is an interconnected mesh, the wavelengths 210 may bedifferent on each of the links 120. Parameters associated with each ofthe wavelengths 210 can include—A-Z path in the network, spectrumallocation (e.g., fixed spectrum, flexible spectrum, amount of spectrum,location on the spectrum, etc.), modulation format, baud rate, FECparameters, optical power, etc.

Referring to FIG. 3, in an exemplary embodiment, a logical networkdiagram illustrates a subset 100 a of the network 100 showing the sites110 a, 110 b, 110 c, 110 e and three exemplary wavelengths 210-1, 210-2,210-3. In the subset 100 a, there is a fiber 200-1 connecting the sites110 a, 110 b, a fiber 200-2 connecting the sites 110 b, 110 c, and afiber 200-3 connecting the sites 110 b, 110 e. The wavelengths 210-1,210-2 are between the sites 110 a, 110 c and through the fibers 200-1,200-2 with an express through at the site 110 b, and the wavelength210-3 is between the sites 110 e, 110 c through the fibers 200-3, 200-2with an express through at the site 110 b. The site 110 a includesmodems 300-1, 300-2, the site 110 c includes modems 300-3, 300-4, 300-5,and the site 110 e includes a modem 300-6. Thus, the wavelength 210-1 isformed by the modems 300-1, 300-3, the wavelength 210-2 is formed by themodems 300-2, 300-5, and the wavelength 210-3 is formed by the modems300-6, 300-4. The various wavelengths 210-1, 210-2, 210-3 can carry anytype of traffic such as, without limitation, Optical Transport Network(OTN), SONET, SDH, Ethernet, Frame Relay, IP, MPLS, and the like as wellof combinations thereof.

Generally, Wavelength Selective Switches (WSSs) and the like areessentially a polychrometer device with multiple output/input ports.Individual wavelengths (i.e., signals) can be switched by such a deviceand relatively sharp roll-offs can be achieved. That is, the WSS may beutilized to provide a demultiplexer function. The WSS can providesignificantly improved roll-off portions from other technologies such asarrayed waveguide gratings (AWGs) or thin film filters (TFFs), however asignificant dead band is still needed for the WSS to separate twosignals. In contrast, coherent modems can separate signals in theelectrical domain which eliminates almost all of the dead band.Advantageously, through such a configuration, deadbands or guardbandsmay be reduced or eliminated.

Superchannels

In an exemplary embodiment, using concatenated optical spectrumtransmission systems and methods and a flexible grid, wavelengths may beconfigured to group A-Z demands together and place signals in thespectrum going on the same path without deadbands between the channelsin the same path. In this manner, such a grouping can be referred to asa “superchannel.” The superchannel grouping is an exemplary techniquethat may be used by the margin-based optimization systems and methods toachieve more bandwidth (higher spectral efficiency) at the expense ofrouting constraints.

Generally, one optical modem is associated with the optical signal whichis the result of modulating an electrical signal onto one opticalcarrier. That electrical signal may have a single carrier such as with asingle Time Division Multiplexing (TDM) stream of QPSK symbols, aplurality of carriers such as with Nyquist Frequency-DivisionMultiplexing (FDM), or a very large number of carriers such as withOrthogonal Frequency-Division Multiplexing (OFDM).

In the more straightforward applications, one optical modem communicatesa bidirectional digital service from a first geographic location to asecond geographic location. A superchannel can be formed by groupinginto a contiguous region of optical spectrum the signals from aplurality of modems that are all following the same path.

The “radio ROADM” technique can be used to coherently multiplex and thendemultiplex a superchannel at a plurality of geographic locations, whilethe superchannel is switched as a single entity by the intermediate WSS.

In an exemplary embodiment, each modem 300 is tunable so that it canselectively generate a wavelength centered at a desired carrierwavelength (or frequency). In exemplary embodiments in which tunablemodem 300 are used, the wavelength range of each modem 300 may be wideenough to enable the modem 300 to generate any wavelength in the opticalspectrum. In other exemplary embodiments, the wavelength range of eachmodem 300 may be wide enough to enable the modem 300 to generate anyoneof a subset of wavelengths in the optical spectrum. The modem 300 may beconfigured to use any of duo-binary, quadrature amplitude modulation(QAM), differential phase shift keying (DPSK), differential quadraturephase shift keying (DQPSK), orthogonal frequency-division multiplexing(OFDM), polarization multiplexing with any of the foregoing, and anyother type of coherent optical modulation and detection technique. It isunderstood that for electronic channel discrimination, a tunable Rx isrequired. In nQAM and nPSK it is achieved using a linear receiver, i.e.a receiver where frequency mixing is taking place between a localoscillator and the incoming signal. The Local Oscillator (LO) needs tobe tuned at the right frequency such that the mixing product can be atbase band where all the necessary filtering will occur. If a receiver isnot operating like above, it requires a tunable optical filter prior tothe optical detector.

Modems

The margin-based optimization systems and methods recognize the abilityof each of the wavelengths 210 to be optimized is based on 1) theunderlying modem's 300 abilities to adjust and 2) the service's needbeing carried by the wavelength 210. The modems 300 can be classified aseither supporting additional capacity or requiring a fixed capacitybased on the functionality of the modem 300. For example, the modems 300can support various different baud rates through software-programmablemodulation formats. The modems 300 can support programmable modulation,or constellations with both varying phase and/or amplitude. In anexemplary embodiment, the flexible optical modem can support multiplecoherent modulation formats such as, for example, i) dual-channel,dual-polarization (DP) binary phase-shift keying (BPSK) for 100 G atsubmarine distances, ii) DP quadrature phase-shift keying (QPSK) for 100G at ultra long haul distances, iii) 16-quadrature amplitude modulation(QAM) for 200 G at metro to regional (600 km) distances), or iv)dual-channel 16QAM for 400 G at metro to regional distances. Thus, in anexemplary embodiment, the same modem 300 can support 100 G to 400 G.With associated digital signal processing (DSP) in the modem 300hardware, moving from one modulation format to another is completelysoftware-programmable.

In another exemplary embodiment, the modem 300 can support N-QAMmodulation formats with and without dual-channel and dual-polarizationwhere N can even be a real number and not necessarily an integer. Here,the modem 300 can support non-standard speeds since N can be a realnumber as opposed to an integer, i.e. not just 100 G, 200 G, or 400 G,but variable speeds, such as 130 G, 270 G, 560 G, etc. These rates couldbe integer multiples of 10 Gb/s, or of 1 Gb/s. Furthermore, with the DSPand software programming, the capacity of the flexible optical modem canbe adjusted upwards or downwards in a hitless manner so as to not affectthe guaranteed rate. In other exemplary embodiments, the modem 300 caninclude hardware which lacks the aforementioned functionality and thussupports a single modulation format/baud rate which cannot be adjusted(but other parameters can be adjusted like power, spectrum location,etc.). Additionally the modems 300 can tune and arbitrarily selectspectrum; thus no optical filters are required. Additionally, the modem300 can support various aspects of nonlinear effect mitigation anddispersion compensation (both for chromatic and polarization mode) inthe electrical domain, thus eliminating external dispersion compensationdevices, filters, etc. Modems can also adapt the forward errorcorrection coding that is used, as another method to trade-off servicerate vs noise tolerance.

In general, the bit rate of the service provided by a modem isproportional to the amount of spectrum occupied, and is a function ofthe noise tolerance. As shown in the examples of FIG. 4, more bitscommunicated generally means less noise tolerance.

In addition to the modem 300 functionality, the optimization can bebased on the service's need being carried by the wavelength 210. Forexample, a time-division multiplexed (TDM) service being offered at afixed capacity may have no requirement to increase its bandwidth. On theother hand, a packet service may need to expand to support morebandwidth, etc. Of course, the packet service could be carried in a TDMservice, e.g., Ethernet over ODUflex. For example, in the subset 100 a,assume the wavelengths 210-1, 210-2 are carrying a service which doesnot need additional capacity or the modems 300-1, 300-2, 300-3, 300-5are incapable of adjusting capacity upwards in the field. Also, assumethe wavelength 210-3 is a service that can take advantage of additionalcapacity, such as by changing the modulation format, increasing baudrate, etc. Again, conventional engineering of the wavelengths 210-1,210-2, 210-3 focuses on forecast tolerance—will the wavelength 210 workat worst case (e.g., full-fill and under end-of-life operatingconditions). In this example, assume the wavelength 210-3 cannot adjustupwards because of the wavelengths 210-1, 210-2, the margin-basedoptimization systems and methods propose techniques to optimize thesewavelengths 210-1, 210-2, 210-3 such that the wavelength 210-3 can useadditional capacity at the expense of the margin of the wavelengths210-1, 210-2.

Referring to FIG. 4, in an exemplary embodiment, a graph illustratesthree exemplary configurations of the modem 300 showing relative noisetolerance (dB) versus bits per dual polarization symbol. Additionally, aline illustrates the Shannon capacity. For illustration purposes herein,the modems 300 are assumed to support 50 GB/s with BPSK, 100 GB/s withQPSK and 200 GB/s with 16QAM, all with the same hardware, i.e. softwareprovisionable. Also, as described above, the modems 300 can achieveother rates such as in-between 50 GB/s, 100 GB/s, and 200 GB/s as wellas below 50 GB/s and above 200 GB/s. This is illustrated with a dottedline in FIG. 4.

The modems 300 can include coherent receivers which require no opticaldispersion compensation or optical filters (multiplexers anddemultiplexers). Also, the modems 300 can support advanced PerformanceMonitoring (PMs) for feedback such as Bit Error Rate (BER), PolarizationDependent Loss (PDL), Polarization Mode Dispersion (PMD), and the liketo provide accurate modeling of optical characteristics. The modems 300include coherent transmitters which can provide spectral shapingallowing for more efficient spectrum use and flexible grid placement.Also, the coherent transmitters support software selectable modulationformat, providing more bits/s/Hz.

Wavelengths that are spaced closer together provide improved spectralefficiency, as discussed. However, nonlinearities such asCross-Phase-Modulation (XPM) generally cause greater degradations whenthere is less channel separation for walk-off. This will reduce thecapacity of each channel. As described herein, a channel or opticalchannel refers to the medium, including switching, filters (WSSs), etc.,which are set up in a network to carry a signal or optical signalbetween two points. A signal or optical signal refers to light havingbeen modulated to carry information which traverses the medium withinthe channel. Also, a wavelength is sometimes used to mean signal oroptical signal. Note, sometimes the term channel is equated to a signalor wavelength, through an implied one-to-one correspondence. The termsuperchannel, as used herein, is used to mean multiple signalstraversing a single channel. Superchannel can also be used to refer to achannel, typically wider in frequency than is normally used for onesignal, which carries multiple signals.

A higher power in a given signal (wavelength) will improve its opticalsignal to noise ratio (OSNR). However, higher powers also cause strongernonlinear effects which are generally degradations to the signal itselfand to other signals. Optical amplifiers, or other optical elements, maylimit the average total output power from that element.

At a given power, a higher Baud rate signal will generally cause lessXPM. The modulation applied to the signal can be designed to induce lessnonlinear degradation in the other signals present on the fiber,generally at a cost of a portion of the bitrate or noise tolerance ofthat modulation.

Depending upon the type of fiber, and any optical dispersioncompensation present, higher Baud rates or lower Baud rates will beadvantageous for minimizing self phase modulation (SPM), at a constantcomposite average power level.

Some kinds of fibers, such as Dispersion Shifted (DS) fiber generatesevere degradations due to Four Wave Mixing (FWM), as a strong functionof the wavelength locations relative to the fiber dispersion zero.

The separation between channels can be squeezed to be less than the Baudrate, and the inevitable resulting linear intersymbol and interchannelinterference reduces the system margin. DSP methods such as MLSE ormultiple channel co-detection can be used to mitigate some of thisreduction. Optical CDMA methods attempt to superimpose multiplewavelengths onto the same spectrum.

Metrics

Referring to FIG. 5, in an exemplary embodiment, a diagram illustrates adashboard 320 showing metrics associated with the optical network 100.The margin-based optimization systems and methods require variousmetrics to determine where optimization is needed. With the modems 300,an associated controller communicatively coupled to the modems 300 isconfigured to compute real-time margin, an estimation, prediction,and/or calculation, showing non-linear impairments, link loss,dispersion, error rates, etc. This information is provided to thephotonic control 150 and/or the SDN controller 160. The photonic control150 uses the information to set associated parameters including perchannel power, amplifier gain, etc. An associated SDN application can beused to adjust and optimize various optimization metrics to see hownoise can be allocated in the network 100 to improve capacity.

The dashboard 320 can be implemented in the SDN application, an EMS, anNMS, etc. to provide instant feedback on the state of the network 100based using the various optimization metrics. In this exemplaryembodiment, the dashboard 320 presents four metrics—health, throughput,excess bandwidth (BW), and Net System Margin (NSM). Health gives anoperator a view of non-blocked restoration paths, how resilient thenetwork 100 is to failures, and restorability in the network 100.Throughput shows how much data is being transported in the network 100,i.e. how much client traffic. The excess bandwidth shows how much excesscapacity is available, e.g. if only 10 GB/s is provisioned on a 100 Gbsline, there is 90% excess.

The margin-based optimization systems and methods can include a NetSystem Margin (NSM). Note, even in a “critically” designed network, itis normal to have excess margin since the network is designed forfull-fill, end of life, fixed modulation formats, safety margin (userdefined with such things as fiber repair and ageing etc.), etc. (i.e.,forecast tolerant). Of note, most of a network's life is spent in acondition which has fewer wavelengths and fewer impairments. Therefore,there is extra margin in most operating conditions and this extra margincan be mined to turn it into capacity to support even more revenuegenerating services. The NSM shows the operator a view of the excessmargin available in the network 100. The NSM can be dB/OSNR and providea view of how much more noise can be handled until the FEC limit. As ananalogy, NSM can be viewed as gas in an automobile gas tank. If there isleftover gas, the automobile (the network 100) can still travel further.

Guaranteed capacity can be defined as that amount that would be presentunder worst-case, end of life, full fill conditions, and Excess capacityis defined as the amount of additional capacity which can be achieved“right now” with acceptable margin. With the margin-based optimizationsystems and methods can include a throughput=total of Guaranteed+Excess;Excess BW=amount exceeded of Guaranteed (in %).

Optimization Metrics

In various exemplary embodiments, the margin-based optimization systemsand methods contemplate using various optimization metrics to adjust theNSM such that the throughput is maximized. The various optimizationmetrics can be viewed as “knobs” which can be turned having anassociated effect on the dashboard 320. The optimization metrics includeanything that is adjusted with the modems 300 and/or the photoniccontrol 150. The optimization metrics can be applied to a singlewavelength, multiple wavelengths, and/or all wavelengths, and differenttechniques can be applied to different wavelengths.

The following is a non-limiting exemplary list of optimizations:

Increasing/Decreasing launch powerModifying wavelengthModifying modulation format including, for example, changing the coding,shaping, power balance, polarization balance, and/or precompensationChanging precompensationIncreasing/Decreasing spectral widthIncreasing/Decreasing spectral shapeIncreasing/Decreasing spectral spacingAdjusting wavelengths across an interconnected mesh for optimizationCreating superchannelsAdjusting amplifier and/or VOA settingsChanging baud rate (which also could change the spectral width, but doesnot have to)Changing FEC parameters (more FEC overhead increased performance, butlowers client data rate)The margin-based optimization systems and methods contemplate using anyof the above, individually or in combination, to make adjustments toincrease throughput.

A good metric captures what is important to the customer in theirparticular optical network, and is amenable to optimization as discussedbelow under “Algorithm”. For example, metrics can be discrete,piece-wise continuous, or differentiable. Examples of applicablemetrics:

-   -   Point-to-point Capacity        -   In-service        -   In-service capacity+equipped capacity        -   In-service+equipped+allocated    -   Point-to-point Available Capacity        -   equipped capacity        -   equipped capacity+allocated spectrum        -   % of total capacity        -   % of in-service capacity    -   Point-to-point Hidden Capacity, i.e. capability to increase the        number of bits per Baud.        -   equipped wavelengths        -   equipped wavelengths+allocated spectrum        -   % of total capacity        -   % of in-service capacity    -   Point-to-point Spectrum        -   In-use, allocated, planned, unallocated        -   GHz, slots, channels, or in units of WS S granularity        -   Weighed by OSNR and nonlinearities present    -   Point-to-point optical power        -   % of allocation is in-service    -   Point-to-point margin under present conditions        -   Minimum across designated wavelengths        -   Minimum across each type of modem        -   Average        -   Surplus above a threshold (being tradable)        -   Sum of tradable margin across all wavelengths        -   Margin in dBs of system gain        -   Margin in capacity relative to Shannon capacity        -   Margin in mW of signal power    -   Margin at EOL (End of Life)    -   Margin under near-term conditions    -   Pre-FEC BER    -   Log-likelihood-ratio histogram into soft FEC    -   Amplified Stimulated Emission (ASE) and nonlinearities;        -   Separate or together.        -   Additive equivalent, in dB relative to signal        -   Micro Watts    -   Cost        -   Equipment count        -   Equipment price    -   Heat    -   DC current    -   Shelf Space    -   Floor Space    -   Latency    -   Availability    -   Capacity-Distance product        -   Gigabit-kilometers        -   Gigabit-line amps        -   Gigabit-dB of attenuation in line        -   Gigabit-WSS    -   Margin-Distance product        -   dB-kilometers        -   dB-dB    -   Combined metrics for a mesh network        -   Average        -   Minimum        -   Maximum        -   Min-max        -   Median        -   Average of median fraction        -   Length**alpha weighted average        -   Calculate across all wavelengths that share any part of a            path with this set of        -   wavelengths        -   Calculate for network subset, with subset choice based upon            criteria such as length or fill.        -   Dilating scope across network        -   Use only shortest path (kilometers or cumulative metric)        -   Use Working, Protection and Restoration paths        -   Redial availability        -   Resilience to failures        -   Blocking events        -   Blocking probabilities for future demands        -   Pinch point fill ratio        -   Number of pinch points        -   Metrics for future demands, such as expected length relative            to the shortest path.    -   Subdividing end-to-end metrics into portions        -   Allocation per segment between WSS        -   Allocation per cable between Erbium-doped Fiber Amplifiers            (EDFAs)        -   Subdividing into metrics which can then be summed across the            length of any route to get the end-to-end metric.

As described herein, each of the aforementioned optimization metrics issoftware configurable in the network 100 with the photonic control 150and/or the SDN controller 160. The question now becomes—what settingsare best in the network 100 based on current conditions to maximize thethroughput to consume excess NSM. Again, note the network 100 isultimately a nonlinear analog system and changes made to one wavelengtheffect other wavelengths. This requires algorithms for rebalancing asdescribed herein.

There are some tradeoffs in the margin-based optimization systems andmethods such as: adding more channels requires more margin—lifetimeeffect in the network, but can be exploited when not at full-fill;channel packing—decreases margin, increases capacity, decreasesflexibility (whole band superchannel—e.g. submarine and point-to-point);mesh demands with superchannels (same A-Z paths for eachsuperchannel)—increases flexibility, increases capacity compared tofixed grid, requires fewer guard bands than fixed grid; modulationformat—increases capacity, requires more margin; and margin basedoptimization, e.g., changing launch power—changes impact on neighboringchannels.

Referring to FIGS. 6-10, in various exemplary embodiments, spectralplots logically illustrate some of the optimization metrics ofchannels/wavelengths. FIG. 6 illustrates two spectrums 502, 504. Thespectrum 502 can be a fixed grid or a flexible grid/gridless spectrumwith more frequency separation between adjacent channels and hencehigher OSNR with excess NSM. The flexible grid or gridless spectrumallows for more dense packing of channels, but comes at a cost to margindue to channel interference, i.e., can pack channels closer together,but the wavelengths cannot go as far, and can reduce their power toreduce interference, but not below the required OSNR. Specifically, thefirst spectrum 502 illustrates channels with more frequency separationand hence higher OSNR whereas as second spectrum 504 illustrates thesame channels packed denser with lower OSNR. The second spectrum 504 canbe a fixed grid or a flexible grid/gridless spectrum with less frequencyseparation between the channels.

FIG. 7 illustrates minimization of filters, i.e., even in flexiblegrids, filters introduce dead bands and unused spectrum, but thisreduces flexibility of channel paths through the network, which forceswavelengths that share a common path to use the same part of thespectrum in order to get the benefits—“express lanes” or superchannels,and if allowed to “overshoot” a destination by staying on an expresslane too long, it takes more “gas” to come back to where it is needed.

FIG. 8 illustrates flexible modulation—in order to pack more bits in thesame amount of spectrum, need to be able to vary modulation formats topack more bits per baud, but this also comes at a cost of margin in thesystem, such as illustrated in FIG. 4. FIG. 9 illustrates higher baudrates—e.g., one 400 G signals consumes spectrum more efficiently thantwo individual 200 G signals, but comes at a cost of reduced signalspath flexibility, and may need to be go further than needed to reach thedestination since the signals are linked electrically. This driveshigher overall margin requirements.

Finally, FIG. 10 illustrates a margin-based optimization process thatcan use an estimate or measurement of NSM each signal has, and penalizesignals who cannot use their margin for the benefit of others. Signalsthat cannot change modulation format or carry additional traffic shouldgive up margin to signals that can change modulation format or tosignals that need or can benefit from excess capacity. In the event of afailure of a signal that is stretched to the limit, attempt to useexcess bandwidth in other signals to provide restoration capacity.

For example, assume FIG. 10 includes the wavelengths 210-1, 210-2, 210-3and FIG. 10 is the optical spectrum for the fiber 200-2. At a first time350, the wavelengths 210-1, 210-2, 210-3 are provisioned, but notoptimized. At times 352, 354, the margin-based optimization isimplemented. Again, as described herein, the wavelengths 210-1, 210-2can have their margins decreased and the wavelength 210-3 can have itsmargin increased (which can be consumed by increasing throughput on thewavelength 210-3). The time 352 shows an intermediate point in themargin-based optimization, and the time 354 shows an end point in themargin-based optimization.

Margin-Based Optimization Method

Referring to FIG. 11, in an exemplary embodiment, a flow chartillustrates a margin-based optimization method 400. The margin-basedequalization method 400 can be implemented in the network 100, via themodems 300, with the photonic control 150, the SDN controller 160, etc.as well as in other networks and components. The margin-basedequalization method 400 includes, for a given network of wavelengthservices, determining which of the modems 300 could provide additionalnetwork capacity (bandwidth) and can actually use the additionalcapacity (typically a switched node with packet services, but notlimited to a switched node.) Each of the wavelength services iscategorized as either margin_increase or margin_decrease (step 402).

Next, the margin-based equalization method 400 computes or retrieves,for each service, its current Required OSNR (ROSNR) and computes orretrieves its forecast tolerant ROSNR (step 404). The ROSNR is the OSNRcurrently required, based on current network conditions, to provide theservice whereas the forecast tolerant ROSNR is the OSNR required, basedon worst case network conditions to provide the service. The OSNRmeasurements can be automatically provided through the modems 300 asdescribed herein.

Next, the margin-based equalization method 400, for all margin_increaseservices, computes current ROSNR and computes its forecast tolerantROSNR for current and higher capacity services and determinesconstraints of the optical line system (step 406). Note, this step canbe constrained to compute values only for services the modem 300 cancarry as well if it is either system limited or switch limited. Next,the margin-based equalization method 400, for every margin_decreaseservice, computes current ROSNR and computes its forecast tolerant ROSNRfor current and alternate modulation schemes that provide equivalentcapacity (step 408).

The margin-based equalization method 400 can include the customer orsystem setting a minimum safety margin to stay away from target ROSNR,and the customer or system determining what ROSNR target margin tooptimize each service against (current network, forecast tolerant orsome other network metric) (step 410). In the margin-based equalizationmethod 400, decreasing OSNR can be achieved, for example, by changingoptical power, altering spectral occupancy on the spectrum, and/oraltering a modulation scheme. Thus, the margin-based equalization method400 can include, for every margin_decrease service well above its targetOSNR for an alternate modulation scheme with a smaller opticalfootprint, performing an adjustment or marking for adjustment its OSNRby altering spectral occupancy and/or modulation scheme. In themargin-based equalization method 400, improving OSNR can be achieved,for example, by changing optical power, altering spectral occupancy,and/or altering a modulation scheme.

The margin-based equalization method 400 performs a loop which includes:for every margin_increase service not able to achieve its highestcapacity and not at max target output power, applying a small increaseto OSNR (e.g. increase power 0.5 dB); for every margin_decrease servicewith an OSNR greater than the target ROSNR, applying a small decrease toOSNR (e.g. decrease power 0.5 dB); computing or retrieving SNR for allservices; noting whether each current OSNR supports a higher capacityand marking for adjustment; providing a small improvement to OSNR formargin_decrease services if needed; if no margin_increase service wasable to improve its OSNR, then the method 400 stops; and continuelooping until all adjustments are less than a nominal hysteresis factor(e.g. 1 dB) or a maximum number of iterations is reached (e.g. 200)(step 414).

If the computed adjustments were performed with an offline tool (not ona live system), then with user intervention or an appropriate intervalor time-of-day, the margin-based equalization method 400 can includeimplementing adjustment of the services. The offline tool can be the SDNapplication. Also, as demand profiles change or system conditionschange, then the margin-based equalization method 400 can be rerun.

Optimization Application and Exemplary Operation

Referring to FIG. 12, in an exemplary embodiment, a network diagramillustrates a network 500 with an interconnected photonic mesh of nodes502A-502K. The nodes 502A-502K can be communicatively coupled to the SDNcontroller 160, and an application 510 can be communicatively coupled tothe SDN controller 160. The nodes 502A-502K can include WDMfunctionality with the modems 300, ROADMs, etc. described herein. Dataassociated with photonic control 150 and margin measurements is providedfrom the nodes 502A-502K to the SDN controller 160, and the SDNcontroller 160 can provide the data to the application 510. Note, thenodes 502A-502K can be actually deployed nodes or simulated nodes. Theapplication 510 is an SDN application which can perform the margin-basedoptimization described herein to monitor metrics, provision services(through the SDN controller 160), adjust capacity and launch powers todemonstrate the tradeoffs with the various optimization metrics, and thelike.

Referring to FIGS. 13-21, in exemplary embodiments, various screens520-1-520-9 illustrate an operation with the application 510 on thenetwork 500. In FIG. 13, the screen 520-1 illustrates the network 520(in a map insert 522) and a service 524 provisioned between the nodes520-B, 520-H. The application 510 provides details 526 of the service524 including, for example, Subnetwork connection (SNC) description,start/end node, margin (16.9 in this case), bandwidth (10 GB/s used outof 100 GB/s), power bias (currently set at 4.0), and modulation type(QPSK). Additionally, the application 510 can display the dashboard 320which shows, in the case of the service 524, that the network 500 ishealthy, has a low throughput, a lot of excess bandwidth (10/100), andvery high NSM. FIG. 14 illustrates a path insert 528 showing a visualrepresentation of a wavelength associated with the service 524. Note,the visual representation can be color coded to show that the wavelengthhas significant NSM.

FIG. 15 illustrates a screen 520-3 with a setup insert 530 whichincludes various options for optimization. These options can include,for example, a single channel optimization, a gridded full optimization,a superchannel full optimization, a gridded mesh optimization, agridless mesh optimization, and a genetic algorithm optimization. Thesingle channel optimization determines an optimization for a singlesignal. The gridded full optimization determines an optimization for allsignals, on a same A-Z path, with each of the signal occupying a channelon the spectral grid. Note, the screen 520-2 in FIG. 15 illustrates agridded full optimization. The superchannel full optimization providesall signals on the same A-Z path in a superchannel. The gridded meshoptimization takes all A-Z demands in the network 500 and optimizesbased on routing signals on the spectral grid through the network 500.The gridless mesh optimization takes all A-Z demands in the network 500and optimizes based on routing signals gridlessly. Finally, the geneticalgorithm optimization determines how A-Z demands should be routed inthe network 500 to optimize superchannels, wavelength placing, etc. toavoid blocking/contention and to maximize throughput.

FIG. 16 illustrates a screen 520-4 with the details 526 of the griddedfull optimization. Note, the margin, for the service 524, has decreasedto 7.5 (from 16.9), the health is low (blocking and no restoration),throughput is higher, there is no excess bandwidth, and NSM hasdecreased. Note, in the path insert 528, the signals can be color codedto denote associated margins. In the gridded full optimization, there isa decrease in margin due to co-propagating signals, i.e. cross-phasemodulation. Also, note that the signals at the edges have higher marginsthan the signals in the middle due to fewer co-propagating channels.FIG. 17 illustrates a screen 520-5 showing what happens to the server524 when the launch power is increased from 4.0 dB to 6.0 dB. This 2 dBincrease causes the margin to go from 7.5 to 2.1 dB due to self-phasemodulation. Also, note the corresponding decrease in the NSM.

FIG. 18 illustrates a screen 520-6 with a superchannel full optimizationof the service 524. Note, in FIGS. 16 and 17, the channels were at 50GHz spacing. Assuming the modems 300 only need 37.5 GHz spectral width,the superchannel full optimization shows all the signals with 40 GHzspacing, in a superchannel from the service 524. Note, in the dashboard320, the health has improved because less spectrum is being used,thereby allowing restoration and removing blocking and contention. Also,the bandwidth has increased to 8800 with a slight decrease in margin(6.1 from 7.9) which is acceptable.

FIG. 19 illustrates a screen 520-7 with the map insert 528 showing theservice 524 with a random fill between other nodes 502 in the network500 in a gridded mesh configuration. Note, traditionally, a firstavailable wavelength is chosen when a new service is provisioned. In themesh situation such as in the network 500, there is not full utilizationdue to blocking/contention because of how the wavelengths are filled.The margin is actually quite high for the service 524 here (15.6) as isthe NSM, but there is wasted spectrum. Typically, only 60-70% of thespectrum can be used in a mesh configuration until wavelength blockingoccurs. One optimization could be to attempt superchannels from theconfiguration in the screen 520-7. In doing this, throughput can beincreased from 26 TB/s (in screen 520-7) to 26.2 TB/s which is not muchbenefit since other wavelengths prohibit the growth of thesuperchannels.

FIG. 20 illustrates a screen 520-8 with the map insert 528 showingresults run by a genetic algorithm or the like to optimize the placementof wavelengths, superchannels, etc. in the network 500. The general rulehere is to provision services as far away from others as possible toallow growth. Note, there are too many variables for manual optimizationby manipulating the optimization metrics. Instead, the genetic algorithmcan perform the optimization which yields an increase in throughput to32.2 TB/s (from 26 TB/s) with high margin and NSM.

FIG. 21 illustrates a screen 520-9 showing a change in the service 524from QPSK (100 GB/s) to 16QAM (200 GB/s). Note, at QPSK, the service 524has a margin of 6.7, but when it switches to 16QAM, the margin drops to0.05 which is unacceptable. Various techniques can be used to improvethe service 524 such as a “Robin Hood” process (stealing from the richsignals to give to the poor). This can include dropping launch power onhigh margin signals. This can improve the margin on the service 524 from0.05 to 0.1 which is still unacceptable. Other techniques can beimplemented such as rebalancing signals (taking all signals to a nominalmargin, such as 4 or 5 dB), repositioning signals (moving wavelengths),creating superchannels, etc. which can get the margin up to 3.0. Note,this is a complicated problem in a nonlinear space where algorithms workbest in tackling the problem.

Also, the application 510 can include a consumption algorithm whichfigures out the best way to convert all signals to the highestmodulation format which can increase throughput from 32.2 TB/s to 52.4TB/s in this example. This provides an added 30 TB/s of traffic withoutnew hardware. This is a significant benefit to service providers.

Referring to FIGS. 22-23, in an exemplary embodiment, spectral diagramsillustrate the optical spectrum before (FIG. 22) and after (FIG. 23)optimization. These spectral diagrams illustrate actual snapshots ofoptical spectrum with various signals that are optimized as describedherein. Note, the application 510 can be used to model the optimization,and once it is finalized, the application 510, through the SDNcontroller 160, can propagate the appropriate commands to the nodes 502to implement the optimization. In this example, FIG. 22 includes QPSKsignals, whereas FIG. 23 includes optimized 16QAM signals which requireless power. Note, changing modulation formats is done with a traffichit, and based on experimentation, it takes several seconds to implementthe changes.

Algorithms

The metrics, constraints, and interrelationships can be approximated bylinear functions. More generally, they are nonlinear, discrete, orpiece-wise continuous. And then there are the optical nonlinear effects.

The margin-based optimization systems and methods can also includeoptimization of the optical nonlinear effects. The basic idea is toemploy a multi-channel, non-linear aware, link modeling routine as partof an objective function for optimization. In addition, one couldinclude the channel capacity as a continuous function (e.g.,modem/transponder modeled Shannon Limit) in the objective function.After the optimization, one would then choose the nearest, bettermargin, option for the modulation/encoding. Once all of the inputs arecast in continuous functions, one can run a non-linear multi-variableoptimizer with constraints (e.g., sequential quadratic programming).

Sequential quadratic programming (SQP) is an iterative method fornonlinear optimization. SQP methods are used on problems for which theobjective function and the constraints are twice continuouslydifferentiable. SQP methods solve a sequence of optimizationsub-problems, each of which optimizes a quadratic model of the objectivesubject to a linearization of the constraints. If the problem isunconstrained, then the method reduces to Newton's method for finding apoint where the gradient of the objective vanishes. If the problem hasonly equality constraints, then the method is equivalent to applyingNewton's method to the first-order optimality conditions, orKarush-Kuhn-Tucker conditions, of the problem. SQP methods have beenimplemented in many packages, including NPSOL, SNOPT, NLPQL, OPSYC,OPTIMA, MATLAB and SQP.

Convex optimization eliminates the differentiability constraint. Geneticalgorithms, and other such heuristics, side-step the need for analysisof the problem space.

It is often useful in nonlinear optimization to be able to model varioussystem parameters as fully defined real functions. (More precisely:having compact support.) Examples of such are continuous functions andpiece-wise continuous functions of real variables; as opposed to onlybeing defined upon integer values. Specifically, modem bit rate, OSNR,even whether or not a signal can support additional capacity, etc. canbe chosen to be modeled as real functions. One can transform thenonlinear optimization problem into something which is solvable by agiven type of algorithm by expressing all inputs to the objectivefunction in a way that meets the constraints of that method. (e.g.locally convex, piece-wise continuous, differentiable, or doublydifferentiable.) A unique aspect here, with the advent of the opticalmodems described herein, is that one of the inputs is the capacity ofthe channel using those modems, which approximately follows parallel toShannon's limit. For the purpose of this optimization, one could justignore the granularity of the bit-rate adjustment, and transform it to afully defined real function which can be fed into the optimizer. Then,after achieving a solution, one could just pick the nearest implementedpoint in the modem.

Depending upon the problem definition and the transformation chosen,optimization methods such as these can be used:

-   -   Non-convex methods        -   Sequential convex programming        -   Convex-concave procedure        -   Alternating convex optimization        -   L1-norm heuristic for cardinality problems        -   Convex relaxation of permutation problems    -   Convex methods that may be applicable to sub-problems        -   Alternating projections        -   Sub gradient methods for non-differentiable functions        -   Decomposition for distributed processing        -   Linear programming        -   Second order cone programming        -   Semi definite programming        -   Geometric programming of posynomials (can be made convex via            transformation)    -   Stochastic programming    -   Nonlinear programming    -   Heuristics        -   Simulated annealing        -   Evolutionary algorithms (genetic)        -   Particle swarm optimization        -   Learning classifier for a neural network        -   Dynamic relaxation        -   Hill-climbing

Some simplifying assumptions can be made to constrain the inputs to thealgorithms such as, for example, no optical dispersion compensation,specific fiber parameters, digital precompensation and post compensationof dispersion, maximum span losses between amplifiers, Erbium-dopedFiber Amplifiers (EDFA) with optional Raman counter-propagatingpreamplifiers, homogeneous model of the EDFAs including ripple and tiltand ignoring spectral hole burning and polarization hole burning,coherent transmission systems, only consider Self-Phase Modulation (SPM)and Cross-Phase Modulation (XPM)—no cross-polarization modulation orStimulated Raman Scattering (SRS) that grow along the amplified line ona linear basis rather than on a power basis, digital precompensation tomitigate some of the SPM, complete power control on a 6.25 GHzgranularity, the required E_(b)/N₀ (the energy per bit to noise powerspectral density ratio) does not change within a product family as afunction of Baud rate, wavelengths can be considered as at most twodisjoint subsets, set A and set B, with priorities or specs orcapacities defined for each, control and transient effects can be lumpedinto simple margin offsets or can be modeled as a statisticaluncertainty on the per channel power, line maintenance, temperature, andaging effects will be ignored, predictions from a model of the marginfor unequipped wavelengths have a specified inaccuracy (Gaussian meanzero in dB), measured margin from a deployed signal will have noise andsmall magnitude discontinuities or hysteresis such as 0.1 dB, and themargin available in any one signal is a function of both the power ofthat signal and the powers of other WDM signals that share at least partof the optical path. Under these assumptions, the Poggiolini model is areasonable approximation to the XPM nonlinearity.

There is a fine granularity set of shaped modulations that trade-offcapacity vs. Gaussian noise tolerance. Beyond the tolerance to “totalGaussian noise” expressed as E_(b)/N₀, different modulations havedifferent tolerances to phase noise from XPM based upon theirconstellation. The contribution of XPM to the total Gaussian noisedepends upon the carrier recovery bandwidth, which is proportional tothe Baud rate. Simplified curves can be provided for this. Differentmodulations have different levels of aggression as interferers on otherWDM signals, as a function of the amount of shaping that is present.Simplified curves can be provided for this.

In an exemplary embodiment, the minimum optical spectrum that one modemsignal requires is 8/7 of the Baud rate, and which can be varied from 5GBaud to 100 GBaud in 5 GBaud increments.

Capacity Boosting Method

Referring to FIG. 24, in an exemplary embodiment, a flow chartillustrates a capacity boosting method 600. The capacity boosting method600 is a method of optimizing capacity of an optical network, such asthe networks 100, 500. The capacity boosting method 600 includesidentifying a first wavelength with an associated target capacity (step602). The first wavelength can be formed with one of the modems 300 andthe associated target capacity can be based on a service being carriedby the first wavelength. The capacity boosting method 600 includesdetermining that the first wavelength has insufficient capability tooperate at the associated target capacity (step 604). The insufficientcapacity can be determined comparing one or more link parametersassociated with the first wavelength to thresholds and deriving the NSM.The one or more link parameters can be any of additive noise,Cross-Phase Modulation, Cross-Polarization Modulation, and spectralwidth. The one or more link parameters can be measured by a modemassociated with the first wavelength or, alternatively, computed orestimated through the application 510.

The capacity boosting method 600 includes adjusting one or morewavelengths to increase capability of the first wavelength such that thefirst wavelength can operate at the associated target capacity (step604). The adjusting can utilize any of modifying average power, changingwavelength, changing modulation, and changing precompensation. Theinsufficient capability can be based on any of noise margin and spectralwidth. The insufficient capability can be not enough to either presentlymeet a performance for the associated target capacity or to meet aperformance for the associated target capacity at a future time (e.g.,next year, next decade, or worst-case network fill over an entire lifeof the equipment). The adjusting can utilize changing modulation to anyof reduce nonlinear aggression, reduce spectral width, and changespectral shape. In an exemplary embodiment, a present capacity of thefirst wavelength is effectively zero or at least 100 GB/s. In anotherexemplary embodiment, the associated target capacity is at least 10 GB/sgreater or lower than the present capacity. In a further exemplaryembodiment, the associated target capacity is approximately equal to thepresent capacity. The first wavelength can be a preexisting signal thatthe method is operated on or a recently added or changed signal.

The method can further include performing a nonlinear optimization todetermine adjustments to the one or more wavelengths by modeling modembit rate, Optical Signal to Noise Ratio (OSNR), and whether or not asignal can support additional capacity as real functions in thenonlinear optimization. Optionally, the adjusting is simulated in anapplication prior to operation on nodes in the optical network. Also,the method can be implemented in a controller or in an optical network.

Robin Hood

Referring to FIG. 25, in an exemplary embodiment, a flow chartillustrates a “Robin Hood” method 620 which intentionally reduces marginon wavelengths for the benefit of other wavelengths. The method 620 is amethod of optimizing capacity of an optical network, such as thenetworks 100, 500, by “robbing from the rich and giving to the poor” interms of wavelengths. That is, the method 620 optimizes capacity of anoptical network, through intentionally reducing margin on one or morewavelengths. The method 620 includes identifying a first wavelengthcapable of using excess capacity (step 622). Here, the first wavelengthis carrying some service such as a packet service that can use excesscapacity. The method 620 includes determining the one or morewavelengths have extra margin (step 624). The excess margin can be basedon the NSM described herein. The method 620 includes adjusting the oneor more wavelengths to reduce associated margin to a nominal marginamount so as to increase supportable capacity of the first wavelength(step 626). For example, the nominal margin can be 3-5 dB. The method620 includes increasing capacity of the first wavelength based on thesupportable capacity (step 628)).

The adjusting can utilize any of modifying average power, changingwavelength, changing modulation, and changing precompensation. Thereduction to the nominal margin can be based on comparing one or morelink parameters associated with one or more wavelengths used to derive aNet System Margin. The one or more link parameters can be any ofadditive noise, Cross-Phase Modulation, Cross-Polarization Modulation,and spectral width. The one or more link parameters can be measured by amodem associated with the one or more wavelengths. The adjusting canutilize changing modulation to any of reduce nonlinear aggression,reduce spectral width, and change spectral shape. The increase incapacity of the first wavelength can be any amount supported by themodem, such as, for example, a factor of two or less, at least 10 GB/smore, etc.

The method can further include performing a nonlinear optimization todetermine adjustments to the one or more wavelengths by modeling modembit rate, Optical Signal to Noise Ratio (OSNR), and whether or not asignal can support additional capacity as real functions in thenonlinear optimization. Optionally, the adjusting is simulated in anapplication prior to operation on nodes in the optical network. Also,the method can be implemented in a controller or in an optical network.

Unblocking a Superchannel

Referring to FIG. 26, in an exemplary embodiment, a flow chartillustrates an unblocking method 640 to increase the supportablecapacity from a first point to a second point in an optical network. Themethod 640 is a method of optimizing capacity of an optical network,such as the networks 100, 500, which can free up spectrum to allow for asuperchannel or the like. The method 640 includes identifying a firstoptical signal that occupies a first portion of optical spectrum fromthe first point to the second point (step 642). The first optical signalis identified based on needing additional capacity, and theidentification can be through any of the various algorithms describedherein. The method 640 includes identifying a second optical signal thatoccupies a second portion of the optical spectrum from the first pointto the second point, wherein the second portion is adjacent to the firstportion (step 644). Here, the second optical signal can be viewed aspreventing future growth of the first optical signal. The method 640includes adjusting the second optical signal to minimize part of orremove all of the second portion that is adjacent to the first opticalsignal to provide a freed up portion of the second portion (step 646);and adjusting the first optical signal to occupy some or all of thefreed up portion (step 648).

The second optical signal can co-propagate with the first optical signalthrough a first node of the optical network and can separately propagatethrough a second node of the optical network. The adjusting the firstoptical signal can be one of converting the first optical signal to asuperchannel and increasing a modulation symbol rate. The adjusting thesecond optical signal can be one of moving the second optical signal toa disjoint portion of the spectrum from the first portion and reducing aspectral width of the second optical signal. The new path can be a thirdportion of the optical spectrum, which is disjoint from the firstportion. The adjusting the second optical signal can be identifying anew path; increasing supported capacity on the new path; andtransferring the freed up portion to the third path. The increasingsupported capacity on the new path can be any of reducing nonlineareffects, increasing spectral width, and increasing power. The increasingsupported capacity on the new path can be any of reducing nonlineareffects, increasing spectral width, and increasing power. The new pathcan be partially disjoint with a route previously taken by the secondoptical signal. The method can further include performing a nonlinearoptimization to determine adjustments to the first optical signal andthe second optical signal by modeling modem bit rate, Optical Signal toNoise Ratio (OSNR), and whether or not a signal can support additionalcapacity as real functions in the nonlinear optimization. Optionally,the adjusting is simulated in an application prior to operation on nodesin the optical network. Also, the method can be implemented in acontroller or in an optical network.

In another exemplary embodiment, a method of increasing the supportablecapacity from a first point to a second point in an optical networkincludes identifying a first optical signal that occupies a firstportion of optical spectrum from the first point to the second point;identifying a second optical signal occupying a second portion ofoptical spectrum that co-propagates with the first signal through afirst element of the optical network, and separately propagates throughat a second element of the optical network; identifying a third path;increasing the supportable capacity of the third path; transferrin atleast a portion of the traffic from the second optical signal to thethird path; freeing up at least a portion of the second optical spectrumthrough the first element; changing the first optical signal to occupyat least some of that freed up-portion; and increasing the capacity ofthe first optical signal.

The change to the first optical signal cam include the addition of acarrier to a superchannel or an increase in modulation symbol rate.Increasing the supportable capacity of the third path can include areduction of nonlinear effects, an increase in spectral width, and/or anincrease in optical power. The third path can include a route that is atleast partially disjoint with the route of the second optical signal.The third path can include a portion of the spectrum that is leastpartially disjoint with the portion of the spectrum occupied by thesecond optical signal. The transferring at least a portion of thetraffic can include moving the wavelength of the second optical signalto the third path, moving the second optical signal away from the firstelement, reducing the traffic carried by the second optical signal, orincreasing the traffic carried by a third optical signal. The freeing upat least a portion of the spectrum can include reducing the spectralwidth of the second optical signal or moving the wavelength of thesecond optical signal.

Planning Wavelength Assignments

Referring to FIG. 27, in an exemplary embodiment, a flow chartillustrates a method 660 of planning wavelength assignments in a WDMmesh optical network, such as the network 100, 500. The method 660includes identifying a first pair of endpoints where there is anexpectation of the traffic demand between those endpoints being greaterthan that which can be directly carried on one carrier (step 662) andallocating spectrum between those endpoints for a first superchannel(step 664). The allocated spectrum has a significant frequencyseparation from other superchannels and a signal between a second pairendpoints occupies spectrum between the first superchannel and anothersuperchannel. Optionally, the allocated spectrum is not presentlyoccupied by signals. A signal that at least partially overlaps theallocated spectrum can be moved or removed within a month or can remainfor longer than a month.

The expected traffic demand can be greater than that which can bedirectly carried on three carriers or a reasonable maximum of what mayoccur from now until the end of a planning window. The amount of trafficwhich can be directly carried on one carrier is the amount thatequipment presently used in the network could reliably carry between theendpoints on one carrier. The superchannel allocation has room for aplurality of modulated carriers or room for a broader modulation thanpresently exists between those endpoints. The significant frequencyseparation can be approximately the maximum possible given ananticipated number of superchannels. Optionally, the first superchannelis the only superchannel between the first endpoints.

Minimizing the Cost of a WDM Mesh Optical Network

Referring to FIG. 28, in an exemplary embodiment, a flow chartillustrates a method 680 of minimizing the cost of a WDM mesh opticalnetwork. The method 680 includes identifying a pair of endpoints (step682); identifying a plurality of optical signals between those endpoints(step 684); adjusting the modulation of a first set of the opticalsignals to increase its traffic capacity (step 686); moving at least aportion of traffic from a second set of the optical signals to the firstset (step 688); and deleting one of the plurality of optical signalsfrom the second set (step 690). The method 680 can include improvingwavelengths in the first set. The improving can include altering a thirdoptical signal, raising the power, and/or increasing the spectral width.

Adjustment of the modulation of the first set can be controlled remotelyby the endpoints. The portion of traffic can be all of the traffic onthe second set, and the second set can be deleted. Optionally, anotherportion of traffic from the second optical signal is moved to a thirdoptical signal. Deleting one the plurality of optical signals caninclude switching to distinct endpoints, switching the wavelength of thesignal, shutting down an output of a transmitter, etc.

Exemplary Network Element

Referring to FIG. 29, in an exemplary embodiment, a block diagramillustrates an exemplary network element 700 for use with the systemsand methods described herein. In an exemplary embodiment, the exemplarynetwork element 700 can be a network element that may consolidate thefunctionality of a multi-service provisioning platform (MSPP), digitalcross connect (DCS), Ethernet and/or Optical Transport Network (OTN)switch, dense wave division multiplexed (DWDM) platform, etc. into asingle, high-capacity intelligent switching system providing Layer 0, 1,and/or 2 consolidation, i.e. a Packet-Optical Transport System (POTS).In another exemplary embodiment, the network element 700 can be any ofan OTN add/drop multiplexer (ADM), a multi-service provisioning platform(MSPP), a digital cross-connect (DCS), an optical cross-connect, anoptical switch, a router, a switch, a wavelength division multiplexing(WDM) terminal, an access/aggregation device, etc. That is, the networkelement 700 can be any digital system with ingress and egress digitalsignals and switching therebetween of signals, timeslots, tributaryunits, etc. While the network element 700 is generally shown as anoptical network element, the systems and methods contemplated for usewith any switching fabric, network element, or network based thereon.

In an exemplary embodiment, the network element 700 includes commonequipment 710, one or more line modules 720, and one or more switchmodules 730. The common equipment 710 can include power; a controlmodule; operations, administration, maintenance, and provisioning(OAM&P) access; user interface ports; and the like. The common equipment710 can connect to a management system 750 through a data communicationnetwork 760 (as well as a Path Computation Element (PCE), SDNcontroller, OpenFlow controller, etc.). The management system 750 caninclude a network management system (NMS), element management system(EMS), or the like. Additionally, the common equipment 710 can include acontrol plane processor, a shelf controller, etc., such as a controller800 illustrated in FIG. 30, configured to operate the control plane asdescribed herein and/or to operate general OAM&P for the network element700. The network element 700 can include an interface 770 forcommunicatively coupling the common equipment 710, the line modules 720,and the switch modules 730 therebetween. For example, the interface 770can be a backplane, mid-plane, a bus, optical or electrical connectors,or the like. The line modules 720 are configured to provide ingress andegress to the switch modules 730 and to external connections on thelinks to/from the network element 700. In an exemplary embodiment, theline modules 720 can form ingress and egress switches with the switchmodules 730 as center stage switches for a three-stage switch, e.g. athree stage Clos switch. Other configurations and/or architectures arealso contemplated. The line modules 720 can include opticaltransceivers, transponders, and/or modems, such as, for example, 1 Gb/s(GbE PHY), 2.5 GB/s (OC-48/STM-1, OTU1, ODU1), 10 Gb/s (OC-192/STM-64,OTU2, ODU2, 10 GbE PHY), 40 Gb/s (OC-768/STM-256, OTU3, ODU3, 40 GbEPHY), 100 Gb/s (OTU4, ODU4, 100 GbE PHY), ODUflex, etc. The line modules720 can include the modems 300.

Further, the line modules 720 can include a plurality of opticalconnections per module and each module may include a flexible ratesupport for any type of connection, such as, for example, 155 MB/s, 622MB/s, 1 GB/s, 2.5 GB/s, 10 GB/s, 40 GB/s, and 100 GB/s, N×1.25 GB/s, andany rate in between. The line modules 720 can include wavelengthdivision multiplexing interfaces, short reach interfaces, and the like,and can connect to other line modules 720 on remote network elements,end clients, edge routers, and the like, e.g. forming connections on thelinks. From a logical perspective, the line modules 720 provide ingressand egress ports to the network element 700, and each line module 720can include one or more physical ports. The switch modules 730 areconfigured to switch signals, timeslots, tributary units, packets,wavelengths, etc. between the line modules 720. For example, the switchmodules 730 can provide wavelength granularity (Layer 0 switching),SONET/SDH granularity such as Synchronous Transport Signal-1 (STS-1) andvariants/concatenations thereof (STS-n/STS-nc), Synchronous TransportModule level 1 (STM-1) and variants/concatenations thereof, VirtualContainer 3 (VC3), etc.; OTN granularity such as Optical Channel DataUnit-1 (ODU1), Optical Channel Data Unit-2 (ODU2), Optical Channel DataUnit-3 (ODU3), Optical Channel Data Unit-4 (ODU4), Optical Channel DataUnit-flex (ODUflex), Optical channel Payload Virtual Containers (OPVCs),ODTUGs, etc.; Ethernet granularity; Digital Signal n (DSn) granularitysuch as DS0, DS1, DS3, etc.; and the like. Specifically, the switchmodules 730 can include Time Division Multiplexed (TDM) (i.e., circuitswitching) and/or packet switching engines. The switch modules 730 caninclude redundancy as well, such as 1:1, 1:N, etc. In an exemplaryembodiment, the switch modules 730 provide OTN switching and/or Ethernetswitching.

Those of ordinary skill in the art will recognize the network element700 can include other components which are omitted for illustrationpurposes, and that the systems and methods described herein iscontemplated for use with a plurality of different network elements withthe network element 700 presented as an exemplary type of a networkelement. For example, in another exemplary embodiment, the networkelement 700 may not include the switch modules 730, but rather have thecorresponding functionality in the line modules 720 (or some equivalent)in a distributed fashion. Alternatively, the network element 700 may notneed the corresponding switching functionality in the case of a WDMterminal. For the network element 700, other architectures providingingress, egress, and switching therebetween are also contemplated forthe systems and methods described herein. In general, the systems andmethods described herein contemplate use with any network elementproviding switching of signals, timeslots, tributary units, wavelengths,etc. and using the control plane. Furthermore, the network element 700is merely presented as one exemplary network element 700 for the systemsand methods described herein.

Managing Excess Capacity

The margin-based optimization systems and methods described hereincreate additional capacity in the networks 100, 500 that has to bemanaged logically. Commonly-assigned U.S. patent application Ser. No.14/176,908, filed Feb. 10, 2014, and entitled, “SYSTEMS AND METHODS FORMANAGING EXCESS OPTICAL CAPACITY AND MARGIN IN OPTICAL NETWORKS,” thecontents of which are incorporated by reference herein, describesvarious exemplary techniques for managing the new capacity provided bythe margin-based optimization systems and methods in the network 100,500. In an exemplary embodiment, a method for managing the excesscapacity includes determining excess margin relative to margin needed toinsure performance at a nominal guaranteed rate associated with aflexible optical modem configured to communicate over an optical link;causing the flexible optical modem to consume most or all of the excessmargin, wherein capacity increased above the nominal guaranteed rate inthe flexible optical modem includes excess capacity; and mapping theexcess capacity to one or more logical interfaces for use by amanagement system, management plane, and/or control plane. Thisleverages the POTS capability as a L0, L1, and/or L2 device which cancreate excess capacity at L0 using the various techniques describedherein and logically mapping this excess capacity in L1 and/or L2 foradditional opportunities.

Shelf Controller

Referring to FIG. 30, in an exemplary embodiment, a block diagramillustrates a controller 800 to provide control plane processing and/oroperations, administration, maintenance, and provisioning (OAM&P) forthe network element 700. The controller 800 can be part of commonequipment, such as common equipment 710 in the network element 700, or astand-alone device communicatively coupled to the network element 700via the DCN 760. The controller 800 can include a processor 810 which isa hardware device for executing software instructions such as operatingthe control plane. The processor 810 can be any custom made orcommercially available processor, a central processing unit (CPU), anauxiliary processor among several processors associated with thecontroller 800, a semiconductor-based microprocessor (in the form of amicrochip or chip set), or generally any device for executing softwareinstructions. When the controller 800 is in operation, the processor 810is configured to execute software stored within memory, to communicatedata to and from the memory, and to generally control operations of thecontroller 800 pursuant to the software instructions. The controller 800can also include a network interface 820, a data store 830, memory 840,an input/output (I/O) interface 850, and the like, all of which arecommunicatively coupled therebetween and with the processor 810.

The network interface 820 can be used to enable the controller 800 tocommunicate on the DCN 760, such as to communicate control planeinformation to other controllers, to the management system 750, to theSDN controller 160, and the like. The network interface 820 can include,for example, an Ethernet card (e.g., 10BaseT, Fast Ethernet, GigabitEthernet) or a wireless local area network (WLAN) card (e.g., 802.11).The network interface 820 can include address, control, and/or dataconnections to enable appropriate communications on the network. Thedata store 830 can be used to store data, such as control planeinformation, provisioning data, OAM&P data, measured metrics, etc. Thedata store 830 can include any of volatile memory elements (e.g., randomaccess memory (RAM, such as DRAM, SRAM, SDRAM, and the like)),nonvolatile memory elements (e.g., ROM, hard drive, flash drive, CDROM,and the like), and combinations thereof. Moreover, the data store 830can incorporate electronic, magnetic, optical, and/or other types ofstorage media. The memory 840 can include any of volatile memoryelements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM,etc.)), nonvolatile memory elements (e.g., ROM, hard drive, flash drive,CDROM, etc.), and combinations thereof. Moreover, the memory 840 mayincorporate electronic, magnetic, optical, and/or other types of storagemedia. Note that the memory 840 can have a distributed architecture,where various components are situated remotely from one another, but maybe accessed by the processor 810. The I/O interface 850 includescomponents for the controller 800 to communicate with other devices.Further, the I/O interface 850 includes components for the controller800 to communicate with the other nodes, such as using overheadassociated with OTN signals.

In an exemplary embodiment, the controller 800 is configured tocommunicate with other controllers 800 in the network 100, 500 tooperate the control plane for control plane signaling. Thiscommunication may be either in-band or out-of-band. For SONET networksand similarly for SDH networks, the controllers 800 may use standard orextended SONET line (or section) overhead for in-band signaling, such asthe Data Communications Channels (DCC). Out-of-band signaling may use anoverlaid Internet Protocol (IP) network such as, for example, UserDatagram Protocol (UDP) over IP. In an exemplary embodiment, thecontrollers 800 can include an in-band signaling mechanism utilizing OTNoverhead. The General Communication Channels (GCC) defined by ITU-TRecommendation G.709 are in-band side channels used to carrytransmission management and signaling information within OpticalTransport Network elements. The GCC channels include GCC0 and GCC1/2.GCC0 are two bytes within the Optical Channel Transport Unit-k (OTUk)overhead that are terminated at every 3R (Re-shaping, Re-timing,Re-amplification) point. GCC1/2 are four bytes (i.e. each of GCC1 andGCC2 include two bytes) within the Optical Channel Data Unit-k (ODUk)overhead. In the present disclosure, GCC0, GCC1, GCC2 or GCC1+2 may beused for in-band signaling or routing to carry control plane traffic.Based on the intermediate equipment's termination layer, different bytesmay be used to carry control plane signaling. If the ODU layer hasfaults, it has been ensured not to disrupt the GCC1 and GCC2 overheadbytes and thus achieving the proper delivery control plane signaling.Other mechanisms are also contemplated for control plane signaling.

The controller 800 can be configured to operate the control plane in thenetwork 100, 500. That is, the controller 800 is configured to implementsoftware, processes, algorithms, etc. that control configurable featuresof the network 100, 500, such as automating discovery of the nodes,capacity on the links, port availability on the nodes, connectivitybetween ports; dissemination of topology and bandwidth informationbetween the nodes; path computation and creation for connections;network level protection and restoration; and the like. As part of thesefunctions, the controller 800 can include a topology database thatmaintains the current topology of the network 100, 500 based on controlplane signaling and a connection database that maintains availablebandwidth on the links again based on the control plane signaling.Again, the control plane is a distributed control plane; thus aplurality of the controllers 800 can act together to operate the controlplane using the control plane signaling to maintain databasesynchronization. In source-based routing, the controller 800 at a sourcenode for a connection is responsible for path computation andestablishing by signaling other controllers 800 in the network 100. Forexample, the originating node and its controller 800 can signal a paththrough various techniques such as Resource Reservation Protocol-TrafficEngineering (RSVP-TE) (G.7713.2), Private Network-to-Network Interface(PNNI), Constraint-based Routing Label Distribution Protocol (CR-LDP),etc. and the path can be signaled as a Designated Transit List (DTL) inPNNI or an Explicit Route Object (ERO) in RSVP-TE/CR-LDP. As describedherein, the connection refers to a signaled, end-to-end connection suchas an SNC, SNCP, LSP, etc. Path computation generally includesdetermining a path, i.e. traversing the links through the nodes from thesource node to the destination node based on a plurality of constraintssuch as administrative weights on the links, bandwidth availability onthe links, etc.

In an exemplary embodiment, the controller 800 can be configured tocompute, calculate, estimate, store, etc. the metrics based on data fromthe modems 300. The controller 800 can store these metrics in the datastore 830 and/or provide the metrics to external devices such as the SDNcontroller 160 via the network interface 820. The metrics can be updatedperiodically as well to provide current, up-to-date information aboutthe photonic layer.

SDN Controller/Service for the Application

Referring to FIG. 31, in an exemplary embodiment, a block diagramillustrates s server 900. The server 900 can be a digital computer that,in terms of hardware architecture, generally includes a processor 902,input/output (I/O) interfaces 904, a network interface 906, a data store908, and memory 910. It should be appreciated by those of ordinary skillin the art that FIG. 31 depicts the server 900 in an oversimplifiedmanner, and a practical embodiment may include additional components andsuitably configured processing logic to support known or conventionaloperating features that are not described in detail herein. Thecomponents (902, 904, 906, 908, and 910) are communicatively coupled viaa local interface 912. The local interface 912 can be, for example butnot limited to, one or more buses or other wired or wirelessconnections, as is known in the art. The local interface 912 can haveadditional elements, which are omitted for simplicity, such ascontrollers, buffers (caches), drivers, repeaters, and receivers, amongmany others, to enable communications. Further, the local interface 912can include address, control, and/or data connections to enableappropriate communications among the aforementioned components.

The processor 902 is a hardware device for executing softwareinstructions. The processor 902 can be any custom made or commerciallyavailable processor, a central processing unit (CPU), an auxiliaryprocessor among several processors associated with the server 900, asemiconductor-based microprocessor (in the form of a microchip or chipset), or generally any device for executing software instructions. Whenthe server 900 is in operation, the processor 902 is configured toexecute software stored within the memory 910, to communicate data toand from the memory 910, and to generally control operations of theserver 900 pursuant to the software instructions. The I/O interfaces 904can be used to receive user input from and/or for providing systemoutput to one or more devices or components. User input can be providedvia, for example, a keyboard, touch pad, and/or a mouse. System outputcan be provided via a display device and a printer (not shown). I/Ointerfaces 904 can include, for example, a serial port, a parallel port,a small computer system interface (SCSI), a serial ATA (SATA), a fibrechannel, Infiniband, iSCSI, a PCI Express interface (PCI-x), an infrared(IR) interface, a radio frequency (RF) interface, and/or a universalserial bus (USB) interface.

The network interface 906 can be used to enable the server 900 tocommunicate on a network. The network interface 906 can include, forexample, an Ethernet card or adapter (e.g., 10BaseT, Fast Ethernet,Gigabit Ethernet, 10 GbE) or a wireless local area network (WLAN) cardor adapter (e.g., 802.11a/b/g/n). The network interface 906 can includeaddress, control, and/or data connections to enable appropriatecommunications on the network. A data store 908 can be used to storedata. The data store 908 can include any of volatile memory elements(e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, and thelike)), nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM,and the like), and combinations thereof. Moreover, the data store 908can incorporate electronic, magnetic, optical, and/or other types ofstorage media. In one example, the data store 908 can be locatedinternal to the server 900 such as, for example, an internal hard driveconnected to the local interface 912 in the server 900. Additionally inanother embodiment, the data store 908 can be located external to theserver 900 such as, for example, an external hard drive connected to theI/O interfaces 904 (e.g., SCSI or USB connection). In a furtherembodiment, the data store 908 can be connected to the server 900through a network, such as, for example, a network attached file server.

The memory 910 can include any of volatile memory elements (e.g., randomaccess memory (RAM, such as DRAM, SRAM, SDRAM, etc.)), nonvolatilememory elements (e.g., ROM, hard drive, tape, CDROM, etc.), andcombinations thereof. Moreover, the memory 910 can incorporateelectronic, magnetic, optical, and/or other types of storage media. Notethat the memory 910 can have a distributed architecture, where variouscomponents are situated remotely from one another, but can be accessedby the processor 902. The software in memory 910 can include one or moresoftware programs, each of which includes an ordered listing ofexecutable instructions for implementing logical functions. The softwarein the memory 910 includes a suitable operating system (O/S) 914 and oneor more programs 916. The operating system 914 essentially controls theexecution of other computer programs, such as the one or more programs516, and provides scheduling, input-output control, file and datamanagement, memory management, and communication control and relatedservices. The one or more programs 916 may be configured to implementthe various processes, algorithms, methods, techniques, etc. describedherein.

In an exemplary embodiment, the SDN controller 160 can be implementedthrough the server 900 where the network interface 908 iscommunicatively coupled to one or more nodes in an optical network. TheSDN controller 160 can also include an Application Programming Interface(API) which allows additional applications to interface with the SDNcontroller 160 for data associated with the optical network. In anexemplary embodiment, the application 510 can be implemented on theserver 900 (or on the server 900 operating as the SDN controller 160)and receive data through the API. Other configurations are alsocontemplated.

Additionally, it will be appreciated that some exemplary embodimentsdescribed herein may include one or more generic or specializedprocessors (“one or more processors”) such as microprocessors, digitalsignal processors, customized processors, and field programmable gatearrays (FPGAs) and unique stored program instructions (including bothsoftware and firmware) that control the one or more processors toimplement, in conjunction with certain non-processor circuits, some,most, or all of the functions of the methods and/or systems describedherein. Alternatively, some or all functions may be implemented by astate machine that has no stored program instructions, or in one or moreapplication specific integrated circuits (ASICs), in which each functionor some combinations of certain of the functions are implemented ascustom logic. Of course, a combination of the aforementioned approachesmay be used. Moreover, some exemplary embodiments may be implemented asa non-transitory computer-readable storage medium having computerreadable code stored thereon for programming a computer, server,appliance, device, etc. each of which may include a processor to performmethods as described and claimed herein. Examples of suchcomputer-readable storage mediums include, but are not limited to, ahard disk, an optical storage device, a magnetic storage device, a ROM(Read Only Memory), a PROM (Programmable Read Only Memory), an EPROM(Erasable Programmable Read Only Memory), an EEPROM (ElectricallyErasable Programmable Read Only Memory), Flash memory, and the like.When stored in the non-transitory computer readable medium, software caninclude instructions executable by a processor that, in response to suchexecution, cause a processor or any other circuitry to perform a set ofoperations, steps, methods, processes, algorithms, etc.

Although the present disclosure has been illustrated and describedherein with reference to preferred embodiments and specific examplesthereof, it will be readily apparent to those of ordinary skill in theart that other embodiments and examples may perform similar functionsand/or achieve like results. All such equivalent embodiments andexamples are within the spirit and scope of the present disclosure, arecontemplated thereby, and are intended to be covered by the followingclaims.

What is claimed is:
 1. A computer-implemented method to increasecapacity of an optical network based on overall excess margin in theoptical network, the method comprising: determining an objectivefunction based on data associated with a plurality of optical signals inthe optical network, each of the optical signals between modems in theoptical network, wherein an input to the objective function compriseshow much margin the optical signals have until Forward Error Correction(FEC) limits are reached; performing an optimization of the objectivefunction based on changing a plurality of parameters of the opticalsignals; and causing changes to settings of a subset of the modems basedon the performing, to change the capacity of the optical network.
 2. Thecomputer-implemented method of claim 1, where the plurality ofparameters comprise average optical power associated with the pluralityof optical signals.
 3. The computer-implemented method of claim 1, wherethe margin of an optical signal further comprises additional margin setto ensure a quality of service over a lifetime of the optical signal. 4.The computer-implemented method of claim 1, wherein the optimization ofthe objective function is performed again after the changes to thesettings of the subset.
 5. The computer-implemented method of claim 1,wherein the margin is based on one or more of measured data andestimated data from nodes in the optical network communicated to one ofa Network Management System (NMS), an Element Management System (EMS), aSoftware Defined Networking (SDN) controller, and a server, wherein themeasured data and the estimated data is utilized to determine the marginin real-time based on one or more of non-linear impairments, link loss,noise, and error rates.
 6. The computer-implemented method of claim 1,wherein the plurality of parameters comprise one or more of per channelpower, amplifier gain, amplifier power, wavelength, precompensation,spectral width, spectral shape, spectral spacing, and superchannelcharacteristics.
 7. The computer-implemented method of claim 1, whereinthe changes to the settings comprise one or more of changes inmodulation format, baud rate, and FEC parameters.
 8. Thecomputer-implemented method of claim 1, further comprising: computingand displaying a dashboard showing a function of the margin of theplurality of optical signals, and one or more additional metrics of theoptical network, wherein the one or more additional metrics comprisehealth detailing a view of non-blocked restoration paths and networkresiliency and restorability, throughput comprising how much data iscurrently being transported in the optical network, and excess bandwidthcomprising how much excess capacity is available in the optical network.9. The computer-implemented method of claim 1, wherein the optimizationcomprises one or more of a per single channel optimization, a griddedfull optimization, a superchannel full optimization, a gridded meshoptimization, and a gridless mesh optimization.
 10. Thecomputer-implemented method of claim 1, wherein the optimizationcomprises a multi-channel, non-linear aware, link modeling routine. 11.The computer-implemented method of claim 1, wherein the optimization hasa plurality of assumptions included based on the optical network toconstrain inputs.
 12. The computer-implemented method of claim 1,wherein the plurality of parameters are associated with paths theoptical signals traverse comprising one or more of frequency spacing,spectrum amount, and optical component settings.
 13. Thecomputer-implemented method of claim 1, wherein the optimizationcomprises a convex optimization.
 14. The computer-implemented method ofclaim 1, wherein the changing a plurality of parameters of the opticalsignals is simulated.
 15. A system comprising one of a NetworkManagement System (NMS), an Element Management System (EMS), a SoftwareDefined Networking (SDN) controller, and a server executing an SDNapplication, adapted to increase capacity of an optical network based onoverall excess margin in the optical network, the system comprising: anetwork interface and a processor communicatively coupled to oneanother; and memory storing instructions that, when executed, cause theprocessor to determine an objective function based on data associatedwith a plurality of optical signals in the optical network, each of theoptical signals between modems in the optical network, wherein an inputto the objective function comprises how much margin the optical signalshave until Forward Error Correction (FEC) limits are reached, perform anoptimization of the objective function based on changes to a pluralityof parameters of the optical signals, and cause changes to settings of asubset of the modems based on the optimization to change the capacity ofthe optical network.
 16. The system of claim 15, wherein the pluralityof parameters comprise one or more of per channel power, amplifier gain,amplifier power, wavelength, precompensation, spectral width, spectralshape, spectral spacing, and superchannel characteristics.
 17. Thesystem of claim 15, wherein the changes to the settings comprise one ormore of changes in modulation format, baud rate, and FEC parameters. 18.The system of claim 15, wherein the memory storing instructions that,when executed, further cause the processor to compute and display adashboard showing a function of the margin of the plurality of opticalsignals, and one or more additional metrics of the optical network,wherein the one or more additional metrics comprise health detailing aview of non-blocked restoration paths and network resiliency andrestorability, throughput comprising how much data is currently beingtransported in the optical network, and excess bandwidth comprising howmuch excess capacity is available in the optical network.
 19. Thecomputer-implemented method of claim 1, wherein the optimization has aplurality of assumptions included based on the optical network toconstrain inputs.
 20. An optical network supporting increases ofcapacity on one or more links based on overall excess margin in theoptical network, the system comprising: a plurality of nodesinterconnected optically to one another by a plurality of links; and aserver communicatively coupled to one or more of the nodes, wherein theserver comprises one of a Network Management System (NMS), an ElementManagement System (EMS), a Software Defined Networking (SDN) controller,and a server executing an SDN application, and wherein the server isadapted to determine an objective function based on data associated witha plurality of optical signals in the optical network, each of theoptical signals between modems at the nodes in the optical network,wherein an input to the objective function comprises how much margin theoptical signals have until Forward Error Correction (FEC) limits arereached, perform an optimization of the objective function based onchanges to a plurality of parameters of the optical signals, and causechanges to settings of a subset of the modems based on the optimizationto change the capacity of the optical network.